MLS-C01 Questions and Answers

Linear regression is a machine learning approach that can be used to solve this problem. Linear regression is a supervised learning technique that can model the relationship between one or more input variables (features) and an output variable (target). In this case, the input variables could be the historical sales data of the part, such as the quarter, the demand, the price, the inventory, etc. The output variable could be the number of units to be produced for the part. Linear regression can learn the coefficients (weights) of the input variables that best fit the output variable, and then use them to make predictions for new data. Linear regression is suitable for problems that involve continuous and numeric output variables, such as predicting house prices, stock prices, or sales volumes. References:

• AWS Machine Learning Specialty Exam Guide
• Linear Regression

The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.

The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non-fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.

The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.

References:

• XGBoost Parameters - Amazon Machine Learning
• Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

The best solution to meet the requirements of the company is to use AWS DataSync to make an initial copy of the entire dataset, and schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS. This is because:

• AWS DataSync is an online data movement and discovery service that simplifies data migration and helps you quickly, easily, and securely transfer your file or object data to, from, and between AWS storage services 1. AWS DataSync can copy data between on-premises object storage and Amazon S3, and also supports encryption, scheduling, monitoring, and data integrity validation 1.
• AWS DataSync can make an initial copy of the entire dataset by using a DataSync agent, which is a software appliance that connects to your on-premises storage and manages the data transfer to AWS 2. The DataSync agent can be deployed as a virtual machine (VM) on your existing hypervisor, or as an Amazon EC2 instance in your AWS account 2.
• AWS DataSync can schedule subsequent incremental transfers of changing data by using a task, which is a configuration that specifies the source and destination locations, the options for the transfer, and the schedule for the transfer 3. You can create a task to run once or on a recurring schedule, and you can also use filters to include or exclude specific files or objects based on their names or prefixes 3.
• AWS DataSync can perform the final cutover from on premises to AWS by using a sync task, which is a type of task that synchronizes the data in the source and destination locations 4. A sync task transfers only the data that has changed or that doesn’t exist in the destination, and also deletes any files or objects from the destination that were deleted from the source since the last sync 4.

Therefore, by using AWS DataSync, the company can create a data repository in the AWS Cloud for machine learning projects, and use Amazon S3 for the data storage, while meeting the requirements of encryption, scheduling, monitoring, and data integrity validation.

References:

• Data Transfer Service - AWS DataSync
• Deploying a DataSync Agent
• Creating a Task
• Syncing Data with AWS DataSync

The best architecture to scale the solution at the lowest cost is to implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance. This option has the following advantages:

• AWS Deep Learning Containers: These are Docker images that are pre-installed and optimized with popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. They can be easily deployed on Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. They can also be integrated with AWS Batch to run containerized batch jobs. Using AWS Deep Learning Containers can simplify the setup and configuration of the deep learning environment and reduce the complexity of the resource management.
• AWS Batch: This is a fully managed service that enables you to run batch computing workloads on AWS. You can define compute environments, job queues, and job definitions to run your batch jobs. You can also use AWS Batch to automatically provision compute resources based on the requirements of the batch jobs. You can specify the type and quantity of the compute resources, such as GPU instances, and the maximum price you are willing to pay for them. You can also use AWS Batch to monitor the status and progress of your batch jobs and handle any failures or interruptions.
• GPU-compatible Spot Instance: This is an Amazon EC2 instance that uses a spare compute capacity that is available at a lower price than the On-Demand price. You can use Spot Instances to run your deep learning training jobs at a lower cost, as long as you are flexible about when your instances run and how long they run. You can also use Spot Instances with AWS Batch to automatically launch and terminate instances based on the availability and price of the Spot capacity. You can also use Spot Instances with Amazon EBS volumes to store your datasets, checkpoints, and logs, and attach them to your instances when they are launched. This way, you can preserve your data and resume your training even if your instances are interrupted.

References:

• AWS Deep Learning Containers
• AWS Batch
• Amazon EC2 Spot Instances
• Using Amazon EBS Volumes with Amazon EC2 Spot Instances

Feature selection is the process of selecting a subset of extracted features that are relevant and contribute to minimizing the error rate of a trained model. Some techniques for feature selection are:

• Correlation plot with heat maps: This technique visualizes the correlation between features using a color-coded matrix. Features that are highly correlated with each other or with the target variable can be identified and removed to reduce redundancy and noise.
• Univariate selection: This technique evaluates each feature individually based on a statistical test, such as chi-square, ANOVA, or mutual information, and selects the features that have the highest scores or p-values. This technique is simple and fast, but it does not consider the interactions between features.
• Feature importance with a tree-based classifier: This technique uses a tree-based classifier, such as random forest or gradient boosting, to rank the features based on their importance in splitting the nodes. Features that have low importance scores can be dropped from the model. This technique can capture the non-linear relationships and interactions between features.

The other options are not techniques for feature selection, but rather for feature engineering, which is the process of creating, transforming, or extracting features from the original data. Feature engineering can improve the performance and interpretability of the model, but it does not reduce the number of features.

• Data scaling with standardization and normalization: This technique transforms the features to have a common scale, such as zero mean and unit variance, or a range between 0 and 1. This technique can help some algorithms, such as k-means or logistic regression, to converge faster and avoid numerical instability, but it does not change the number of features.
• Data binning: This technique groups the continuous features into discrete bins or categories based on some criteria, such as equal width, equal frequency, or clustering. This technique can reduce the noise and outliers in the data, and also create ordinal or nominal features that can be used for some algorithms, such as decision trees or naive Bayes, but it does not reduce the number of features.
• Data augmentation: This technique generates new data from the existing data by applying some transformations, such as rotation, flipping, cropping, or noise addition. This technique can increase the size and diversity of the data, and help prevent overfitting, but it does not reduce the number of features.

References:

• Feature engineering - Machine Learning Lens
• Amazon SageMaker Autopilot now provides feature selection and the ability to change data types while creating an AutoML experiment
• Feature Selection in Machine Learning | Baeldung on Computer Science
• Feature Selection in Machine Learning: An easy Introduction

The most secure action to protect the data from being accessed and transferred to a remote host by malicious code accidentally installed on the training container is to enable network isolation for training jobs. Network isolation is a feature that allows you to run training and inference containers in internet-free mode, which blocks any outbound network calls from the containers, even to other AWS services such as Amazon S3. Additionally, no AWS credentials are made available to the container runtime environment. This way, you can prevent unauthorized access to your data and resources by malicious code or users. You can enable network isolation by setting the EnableNetworkIsolation parameter to True when you call CreateTrainingJob, CreateHyperParameterTuningJob, or CreateModel.

References:

• Run Training and Inference Containers in Internet-Free Mode - Amazon SageMaker

The best way to meet the requirements is to use a principal component analysis (PCA) model, which is a technique that reduces the dimensionality of the dataset by transforming the original variables into a smaller set of new variables, called principal components, that capture most of the variance and information in the data1. This technique has the following advantages:

• It can increase the accuracy of the model by removing noise, redundancy, and multicollinearity from the data, and by enhancing the interpretability and generalization of the model23.
• It can decrease the processing time of the model by reducing the number of features and the computational complexity of the model, and by improving the convergence and stability of the model45.
• It is suitable for numeric variables, as it relies on the covariance or correlation matrix of the data, and it can handle a large quantity of variables, as it can extract the most relevant ones16.

The other options are not effective or appropriate, because they have the following drawbacks:

• A: Creating new features and interaction variables can increase the accuracy of the model by capturing more complex and nonlinear relationships in the data, but it can also increase the processing time of the model by adding more features and increasing the computational complexity of the model7. Moreover, it can introduce more noise, redundancy, and multicollinearity in the data, which can degrade the performance and interpretability of the model8.
• C: Applying normalization on the feature set can increase the accuracy of the model by scaling the features to a common range and avoiding the dominance of some features over others, but it can also decrease the processing time of the model by reducing the numerical instability and improving the convergence of the model . However, normalization alone is not enough to address the high dimensionality and high latency issues of the dataset, as it does not reduce the number of features or the variance in the data.
• D: Using a multiple correspondence analysis (MCA) model is not suitable for numeric variables, as it is a technique that reduces the dimensionality of the dataset by transforming the original categorical variables into a smaller set of new variables, called factors, that capture most of the inertia and information in the data. MCA is similar to PCA, but it is designed for nominal or ordinal variables, not for continuous or interval variables.

References:

• 1: Principal Component Analysis - Amazon SageMaker
• 2: How to Use PCA for Data Visualization and Improved Performance in Machine Learning | by Pratik Shukla | Towards Data Science
• 3: Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls | by Nagesh Singh Chauhan | Towards Data Science
• 4: How to Reduce Dimensionality with PCA and Train a Support Vector Machine in Python | by James Briggs | Towards Data Science
• 5: Dimensionality Reduction and Its Applications | by Aniruddha Bhandari | Towards Data Science
• 6: Principal Component Analysis (PCA) in Python | by Susan Li | Towards Data Science
• 7: Feature Engineering for Machine Learning | by Dipanjan (DJ) Sarkar | Towards Data Science
• 8: Feature Engineering — How to Engineer Features and How to Get Good at It | by Parul Pandey | Towards Data Science
• : [Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science]
• : [Why, How and When to Scale your Features | by George Seif | Towards Data Science]
• : [Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science]
• : [Multiple Correspondence Analysis - Amazon SageMaker]
• : [Multiple Correspondence Analysis (MCA) | by Raul Eulogio | Towards Data Science]

The FindMatches transform is a machine learning transform that can identify and match similar records from different datasets, even when the records do not have a common unique identifier or exact field values. The FindMatches transform can also remove duplicate records from a single dataset. The FindMatches transform can be used with AWS Glue crawlers and jobs to process the data from various sources and store it in a data lake. The FindMatches transform can be created and managed using the AWS Glue console, API, or AWS Glue Studio.

The other options are not suitable for this use case because:

• Option A: Using an AWS Lambda function to process the data and compare equal strings in the fields from the two datasets is not an efficient or scalable solution. It would require writing custom code and handling the data loading and cleansing logic. It would also not account for variations or inconsistencies in the field values, such as spelling errors, abbreviations, or missing data.
• Option B: The AWS Glue SearchTables API operation is used to search for tables in the AWS Glue Data Catalog based on a set of criteria. It is not a machine learning transform that can match records across different datasets or remove duplicates. It would also require writing custom code to invoke the API and process the results.
• Option D: AWS Lake Formation does not provide a custom transform feature. It provides predefined blueprints for common data ingestion scenarios, such as database snapshot, incremental database, and log file. These blueprints do not support matching records across different datasets or removing duplicates.

• The Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. However, the Data Scientist observes that there is a wide gap between the training and validation set accuracy, which indicates that the model is overfitting the data and generalizing poorly to new data.
• To improve the model performance and satisfy the Marketing team’s needs, the Data Scientist can use the following methods:

References:

• Regularization for Logistic Regression
• Recursive Feature Elimination

Amazon SageMaker supports encryption at rest for the ML storage volumes, the model artifacts, and the data in Amazon S3 using AWS Key Management Service (AWS KMS). AWS KMS is a service that allows customers to create and manage encryption keys that can be used to encrypt data. AWS KMS also provides an audit trail of key usage by logging key events to AWS CloudTrail. Customers can use either AWS managed keys or customer managed keys to encrypt their data. AWS managed keys are created and managed by AWS on behalf of the customer, while customer managed keys are created and managed by the customer. Customer managed keys offer more control and flexibility over the key policies, permissions, and rotation. Therefore, to meet the requirements of the company, the best option is to use customer managed keys in AWS KMS to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

The other options are not correct because:

• Option A: AWS Cloud HSM is a service that provides hardware security modules (HSMs) to store and use encryption keys. AWS Cloud HSM is not integrated with Amazon SageMaker, and cannot be used to encrypt the ML data volumes, the model artifacts, or the data in Amazon S3. AWS Cloud HSM is more suitable for customers who need to meet strict compliance requirements or who need direct control over the HSMs.
• Option B: SageMaker built-in transient keys are temporary keys that are used to encrypt the ML data volumes and are discarded immediately after encryption. These keys do not provide persistent encryption or logging of key usage. Enabling default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes does not affect the ML data volumes, which are encrypted separately by SageMaker. Moreover, this option does not address the encryption of the model artifacts and data in Amazon S3.
• Option D: AWS Security Token Service (AWS STS) is a service that provides temporary credentials to access AWS resources. AWS STS does not provide encryption keys or encryption services. AWS STS cannot be used to encrypt the ML storage volumes, the model artifacts, or the data in Amazon S3.

References:

• Protect Data at Rest Using Encryption - Amazon SageMaker
• What is AWS Key Management Service? - AWS Key Management Service
• What is AWS CloudHSM? - AWS CloudHSM
• What is AWS Security Token Service? - AWS Security Token Service

 Based on the figure provided, a decision tree would have the highest recall with respect to the fraudulent class. Recall is a model evaluation metric that measures the proportion of actual positive instances that are correctly classified by the model. Recall is calculated as follows:

Recall = True Positives / (True Positives + False Negatives)

A decision tree is a type of machine learning model that can perform classification tasks by splitting the data into smaller and purer subsets based on a series of rules or conditions. A decision tree can handle both linear and non-linear data, and can capture complex patterns and interactions among the features. A decision tree can also be easily visualized and interpreted1

In this case, the data is not linearly separable, and has a clear pattern of seasonality. The fraudulent class forms a large circle in the center of the plot, while the normal class is scattered around the edges. A decision tree can use the transaction month and the age of account as the splitting criteria, and create a circular boundary that separates the fraudulent class from the normal class. A decision tree can achieve a high recall for the fraudulent class, as it can correctly identify most of the black dots as positive instances, and minimize the number of false negatives. A decision tree can also adjust the depth and complexity of the tree to balance the trade-off between recall and precision23

The other options are not valid or suitable for achieving a high recall for the fraudulent class. A linear support vector machine (SVM) is a type of machine learning model that can perform classification tasks by finding a linear hyperplane that maximizes the margin between the classes. A linear SVM can handle linearly separable data, but not non-linear data. A linear SVM cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall4 A naive Bayesian classifier is a type of machine learning model that can perform classification tasks by applying the Bayes’ theorem and assuming conditional independence among the features. A naive Bayesian classifier can handle both linear and non-linear data, and can incorporate prior knowledge and probabilities into the model. However, a naive Bayesian classifier may not perform well when the features are correlated or dependent, as in this case. A naive Bayesian classifier may not capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall5 A single perceptron with sigmoidal activation function is a type of machine learning model that can perform classification tasks by applying a weighted linear combination of the features and a non-linear activation function. A single perceptron with sigmoidal activation function can handle linearly separable data, but not non-linear data. A single perceptron with sigmoidal activation function cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall.

To prepare the data for Word2Vec, the Specialist needs to perform some preprocessing steps that can help reduce the noise and complexity of the data, as well as improve the quality of the embeddings. Some of the common preprocessing steps for Word2Vec are:

• Normalizing all words by making the sentence lowercase: This can help reduce the vocabulary size and treat words with different capitalizations as the same word. For example, “Fox” and “fox” should be considered as the same word, not two different words.
• Removing stop words using an English stopword dictionary: Stop words are words that are very common and do not carry much semantic meaning, such as “the”, “a”, “and”, etc. Removing them can help focus on the words that are more relevant and informative for the task.
• Tokenizing the sentence into words: Tokenization is the process of splitting a sentence into smaller units, such as words or subwords. This is necessary for Word2Vec, as it operates on the word level and requires a list of words as input.

The other options are not necessary or appropriate for Word2Vec:

• Performing part-of-speech tagging and keeping the action verb and the nouns only: Part-of-speech tagging is the process of assigning a grammatical category to each word, such as noun, verb, adjective, etc. This can be useful for some natural language processing tasks, but not for Word2Vec, as it can lose some important information and context by discarding other words.
• Correcting the typography on “quck” to “quick”: Typo correction can be helpful for some tasks, but not for Word2Vec, as it can introduce errors and inconsistencies in the data. For example, if the typo is intentional or part of a dialect, correcting it can change the meaning or style of the sentence. Moreover, Word2Vec can learn to handle typos and variations in spelling by learning similar embeddings for them.
• One-hot encoding all words in the sentence: One-hot encoding is a way of representing words as vectors of 0s and 1s, where only one element is 1 and the rest are 0. The index of the 1 element corresponds to the word’s position in the vocabulary. For example, if the vocabulary is [“cat”, “dog”, “fox”], then “cat” can be encoded as [1, 0, 0], “dog” as [0, 1, 0], and “fox” as [0, 0, 1]. This can be useful for some machine learning models, but not for Word2Vec, as it does not capture the semantic similarity and relationship between words. Word2Vec aims to learn dense and low-dimensional embeddings for words, where similar words have similar vectors.

 To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:

• Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.
• Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.

The other options are not valid because:

• Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.
• Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.
• Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:
• Amazon Translate
• Amazon Comprehend
• Amazon SageMaker
• Amazon SageMaker Neural Topic Model (NTM) Algorithm
• Amazon SageMaker BlazingText
• Amazon SageMaker Seq2Seq
• Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm
• Amazon Lex

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:

• Amazon Comprehend - Amazon Web Services
• AWS Certified Machine Learning - Specialty Sample Questions

The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job. References: AWS Glue, Amazon SageMaker

Recall and precision are two metrics that can be used to evaluate the performance of a classification model. Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:

• Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92
• Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8

Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

 The best option is to deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. This way, the inference can be performed locally on the edge devices, without the need to upload the images to Amazon S3 and invoke the SageMaker endpoint. This will reduce the latency and the network bandwidth consumption. The long-running Lambda function can be extended to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service. This will also simplify the architecture and eliminate the dependency on the internet connection.

Option A is not cost-effective, as it requires setting up a 10 Gbps AWS Direct Connect connection and increasing the size and number of instances for the SageMaker endpoint. This will increase the operational costs and complexity.

Option B is not optimal, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Compressing and decompressing the images will add additional processing overhead and latency.

Option C is not sufficient, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Auto scaling for SageMaker will help to handle the increased workload, but it will not reduce the latency or the network bandwidth consumption. Setting up an AWS Direct Connect connection will improve the network performance, but it will also increase the operational costs and complexity. References:

• AWS IoT Greengrass
• Deploying Machine Learning Models to Edge Devices
• AWS Certified Machine Learning - Specialty Exam Guide

 The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

The other options are not as effective as option C for the following reasons:

• Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.
• Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.
• Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.

References:

• Amazon Transcribe – Custom Vocabulary
• Amazon Transcribe – Custom Language Models
• [Amazon Lex – Limits]

The solution A will meet the requirements with the least development effort because it uses Amazon Kendra, which is a highly accurate and easy to use intelligent search service powered by machine learning. Amazon Kendra can index company documents from various sources and formats, such as PDF, HTML, Word, and more. Amazon Kendra can also integrate with chatbots by using the Amazon Kendra Query API operation, which can understand natural language questions and provide relevant answers from the indexed documents. Amazon Kendra can also provide additional information, such as document excerpts, links, and FAQs, to enhance the chatbot experience1.

The other options are not suitable because:

• Option B: Training a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents, deploying the model as a real-time Amazon SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BiDAF network, which is a complex deep learning model for question answering. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic2.
• Option C: Training an Amazon SageMaker BlazingText model based on past customer questions and company documents, deploying the model as a real-time SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BlazingText model, which is a fast and scalable text classification and word embedding algorithm. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic3.
• Option D: Indexing company documents by using Amazon OpenSearch Service and integrating the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation will not meet the requirements effectively. Amazon OpenSearch Service is a fully managed service that provides fast and scalable search and analytics capabilities. However, it is not designed for natural language question answering, and it may not provide accurate or relevant answers for the chatbot. Moreover, the k-NN Query API operation is used to find the most similar documents or vectors based on a distance function, not to find the best answers based on a natural language query4.

References:

• 1: Amazon Kendra
• 2: Bidirectional Attention Flow for Machine Comprehension
• 3: Amazon SageMaker BlazingText
• 4: Amazon OpenSearch Service

The SageMaker image classification algorithm supports both RecordIO and image content types for training in file mode, and supports the RecordIO content type for training in pipe mode1. However, the algorithm also supports training in pipe mode using the image files without creating RecordIO files, by using the augmented manifest format2. In this case, the company should generate

 The business problem of predicting whether a new credit card applicant will default on a credit card payment can be framed as a binary classification problem. Binary classification is the task of predicting a discrete class label output for an example, where the class label can only take one of two possible values. In this case, the class label can be either “default” or “no default”, indicating whether the applicant will or will not default on a credit card payment. A binary classification model can return the probability that a given applicant belongs to each class, and then assign the applicant to the class with the highest probability. For example, if the model predicts that an applicant has a 0.8 probability of defaulting and a 0.2 probability of not defaulting, then the model will classify the applicant as “default”. Binary classification is suitable for this problem because the outcome of interest is categorical and binary, and the model needs to return the probability of each outcome.

References:

• AWS Machine Learning Specialty Exam Guide
• AWS Machine Learning Training - Classification vs Regression in Machine Learning

The best indicators that an ML-based solution is suitable for this scenario are D and E, because they imply that the historical sensor data is sufficient and relevant for building a predictive maintenance model. This model can use machine learning techniques such as regression, classification, or anomaly detection to learn from the past data and forecast future failures or issues12. Having high granularity and diversity of data can improve the accuracy and generalization of the model, as well as enable the detection of complex patterns and relationships that are not captured by simple rule-based thresholds3.

The other options are not good indicators that an ML-based solution is suitable, because they suggest that the historical sensor data is incomplete, inconsistent, or inadequate for building a predictive maintenance model. These options would require additional data collection, preprocessing, or augmentation to overcome the data quality issues and ensure that the model can handle different scenarios and types of cranes4 .

References:

• 1: Machine Learning Techniques for Predictive Maintenance
• 2: A Guide to Predictive Maintenance & Machine Learning
• 3: Machine Learning for Predictive Maintenance: Reinventing Asset Upkeep
• 4: Predictive Maintenance with Machine Learning: A Complete Guide
• : [Machine Learning for Predictive Maintenance - AWS Online Tech Talks]

The best technique to improve the recall of the MLP for the target class of interest is to add class weights to the MLP’s loss function and then retrain. Class weights are a way of assigning different importance to each class in the dataset, such that the model will pay more attention to the classes with higher weights. This can help mitigate the class imbalance problem, where the model tends to favor the majority class and ignore the minority class. By increasing the weight of the target class of interest, the model will try to reduce the false negatives and increase the true positives, which will improve the recall metric. Adding class weights to the loss function is also a quick and easy solution, as it does not require gathering more data, changing the model architecture, or switching to a different algorithm.

References:

• AWS Machine Learning Specialty Exam Guide
• AWS Machine Learning Training - Deep Learning with Amazon SageMaker
• AWS Machine Learning Training - Class Imbalance and Weighted Loss Functions

The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.

Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.

Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.

Option D is incorrect because using Amazon Rekognition’s feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.

References:

• Amazon Augmented AI
• Amazon SageMaker Ground Truth
• Amazon Textract Operations
• Amazon Rekognition

A naive Bayesian model assumes that the features are conditionally independent given the class label. This means that the joint probability of the features and the class can be factorized as the product of the class prior and the feature likelihoods. A full Bayesian network, on the other hand, does not make this assumption and allows for modeling arbitrary dependencies between the features and the class using a directed acyclic graph. In this case, the joint probability of the features and the class is given by the product of the conditional probabilities of each node given its parents in the graph. If the features are statistically dependent, meaning that their correlation coefficients are not close to zero, then a naive Bayesian model would not capture these dependencies and would likely perform worse than a full Bayesian network that can account for them. Therefore, a full Bayesian network describes the underlying data better in this situation. References:

• Naive Bayes and Text Classification I
• Bayesian Networks

 The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.

References:

• Fraud Detection Using Machine Learning | Implementations | AWS Solutions
• Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
• 1. Introduction — Reproducible Machine Learning for Credit Card Fraud Detection

The solution D is the best data visualization approach to determine the optimal value of k for the k-means clustering algorithm. The solution D involves the following steps:

• Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). The SSE is a measure of how well the clusters fit the data. It is calculated by summing the squared distances of each data point to its closest cluster center. A lower SSE indicates a better fit, but it will always decrease as the number of clusters increases. Therefore, the goal is to find the smallest value of k that still has a low SSE1.
• Plot a line chart of the SSE for each value of k. The line chart will show how the SSE changes as the value of k increases. Typically, the line chart will have a shape of an elbow, where the SSE drops rapidly at first and then levels off. The optimal value of k is the point after which the curve starts decreasing in a linear fashion. This point is also known as the elbow point, and it represents the balance between the number of clusters and the SSE1.

The other options are not suitable because:

• Option A: Calculating the principal component analysis (PCA) components, running the k-means clustering algorithm for a range of k by using only the first two PCA components, and creating a scatter plot with a different color for each cluster will not accurately determine the optimal value of k. PCA is a technique that reduces the dimensionality of the data by transforming it into a new set of features that capture the most variance in the data. However, PCA may not preserve the original structure and distances of the data, and it may lose some information in the process. Therefore, running the k-means clustering algorithm on the PCA components may not reflect the true clusters in the data. Moreover, using only the first two PCA components may not capture enough variance to represent the data well. Furthermore, creating a scatter plot may not be reliable, as it depends on the subjective judgment of the data scientist to decide when the clusters look reasonably separated2.
• Option B: Calculating the PCA components and creating a line plot of the number of components against the explained variance will not determine the optimal value of k. This approach is used to determine the optimal number of PCA components to use for dimensionality reduction, not for clustering. The explained variance is the ratio of the variance of each PCA component to the total variance of the data. The optimal number of PCA components is the point where adding more components does not significantly increase the explained variance. However, this number may not correspond to the optimal number of clusters, as PCA and k-means clustering have different objectives and assumptions2.
• Option C: Creating a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values will not determine the optimal value of k. t-SNE is a technique that reduces the dimensionality of the data by embedding it into a lower-dimensional space, such as a two-dimensional plane. t-SNE preserves the local structure and distances of the data, and it can reveal clusters and patterns in the data. However, t-SNE does not assign labels or centroids to the clusters, and it does not provide a measure of how well the clusters fit the data. Therefore, t-SNE cannot determine the optimal number of clusters, as it only visualizes the data. Moreover, t-SNE depends on the perplexity parameter, which is a measure of how many neighbors each point considers. The perplexity parameter can affect the shape and size of the clusters, and there is no optimal value for it. Therefore, creating a t-SNE plot for a range of perplexity values may not be consistent or reliable3.

References:

• 1: How to Determine the Optimal K for K-Means?
• 2: Principal Component Analysis
• 3: t-Distributed Stochastic Neighbor Embedding

The best way to handle the missing values in the patient age feature is to replace them with the mean or median value from the dataset. This is a common technique for imputing missing values that preserves the overall distribution of the data and avoids introducing bias or reducing the sample size. Dropping the records or the feature would result in losing valuable information and reducing the accuracy of the model. Using k-means clustering would not be appropriate for handling missing values in a single feature, as it is a method for grouping similar data points based on multiple features.

References:

• Effective Strategies to Handle Missing Values in Data Analysis
• How To Handle Missing Values In Machine Learning Data With Weka
• How to handle missing values in Python - Machine Learning Plus

The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user’s preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.

The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN-Metadata recipe in Amazon Personalize can help capture the user’s attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.

References:

• Recording events - Amazon Personalize
• Using real-time events - Amazon Personalize

A logarithmic transformation is a suitable data transformation for a linear regression model when the data has a skewed distribution, such as when the mode is lower than the median and the median is lower than the mean. A logarithmic transformation can reduce the skewness and make the data more symmetric and normally distributed, which are desirable properties for linear regression. A logarithmic transformation can also reduce the effect of outliers and heteroscedasticity (unequal variance) in the data. An exponential transformation would have the opposite effect of increasing the skewness and making the data more asymmetric. A polynomial transformation may not be able to capture the nonlinearity in the data and may introduce multicollinearity among the transformed variables. A sinusoidal transformation is not appropriate for data that does not have a periodic pattern.

References:

• Data Transformation - Scaler Topics
• Linear Regression - GeeksforGeeks
• Linear Regression - Scribbr

 Early stopping is a technique that can be used to prevent overfitting and improve model performance on the validation set. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the graph, where the training loss keeps decreasing, but the validation loss starts to increase after some point. This means that the model is fitting the noise and patterns in the training data that are not relevant for the validation data. Early stopping is a way of stopping the training process before the model overfits the training data. It works by monitoring the validation loss and stopping the training when the validation loss stops decreasing or starts increasing. This way, the model is saved at the point where it has the best performance on the validation set. Early stopping can also save time and resources by reducing the number of epochs needed for training. References:

• Early Stopping
• How to Stop Training Deep Neural Networks At the Right Time Using Early Stopping

AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating data. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.

The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.

References:

• 1: AWS Glue - Fully Managed ETL Service - Amazon Web Services
• 2: Amazon EMR - Amazon Web Services
• 3: Amazon Athena – Interactive SQL Queries for Data in Amazon S3
• [4]: AWS Lambda – Serverless Compute - Amazon Web Services

Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:

• XGBoost Parameters

• Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.
• To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.
• The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.

References:

• What Is Amazon Kinesis Data Analytics for SQL Applications?
• Using AWS Lambda with Amazon Kinesis Data Streams
• Using AWS Lambda with Amazon Kinesis Data Firehose

 The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data ©. References:

• Amazon SageMaker Developer Guide: Train and Test Datasets
• Amazon Personalize Developer Guide: Preparing and Importing Data

The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.

References:

• Access Training Data - Amazon SageMaker
• Choosing Data Input Mode Using the SageMaker Python SDK - Amazon SageMaker
• CreateTrainingJob - Amazon SageMaker Service

Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker’s prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.

Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.

References:

• Bring your own model with Amazon SageMaker script mode
• GitHub - aws-samples/amazon-sagemaker-script-mode
• Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3
• amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master

The correct solution for granting permissions for data preprocessing is to use the following steps:

• Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. This role allows the ML specialist to run Processing jobs from the notebook code1
• Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. This role allows the Processing job to access the data in the encrypted S3 bucket, decrypt it with the KMS CMK, and pull the container image from ECR23

The other options are incorrect because they either miss some permissions or use unnecessary steps. For example:

• Option A uses a single IAM role for both the notebook instance and the Processing job. This role may have more permissions than necessary for the notebook instance, which violates the principle of least privilege4
• Option C sets up both an S3 endpoint and a KMS endpoint in the default VPC. These endpoints are not required for the Processing job to access the data in the encrypted S3 bucket. They are only needed if the Processing job runs in network isolation mode, which is not specified in the question.
• Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.

References:

• 1: Create an Amazon SageMaker Notebook Instance - Amazon SageMaker
• 2: Create a Processing Job - Amazon SageMaker
• 3: Use AWS KMS–Managed Encryption Keys - Amazon Simple Storage Service
• 4: IAM Best Practices - AWS Identity and Access Management
• : Network Isolation - Amazon SageMaker
• : Understanding and Getting Your Security Credentials - AWS General Reference

Amazon Rekognition is a service that provides computer vision capabilities for image and video analysis, such as object, scene, and activity detection, face and text recognition, and custom label detection. Amazon Rekognition can be used to automate the quality control process for plaques by comparing the images of the plaques with the images of defects in the Amazon S3 bucket and returning a confidence score for each defect. Amazon A2I is a service that enables human review of machine learning predictions, such as low-confidence predictions from Amazon Rekognition. Amazon A2I can be integrated with a private workforce option, which allows the engraving company to use its own internal team of reviewers to manually inspect the plaques that are flagged by Amazon Rekognition. This solution meets the requirements of automating the quality control process, sending low-confidence predictions to an internal team of reviewers, and using Amazon A2I for manual review. References:

• 1: Amazon Rekognition documentation
• 2: Amazon A2I documentation
• 3: Amazon Rekognition Custom Labels documentation
• 4: Amazon A2I Private Workforce documentation

Pipe input mode is a feature of Amazon SageMaker that allows streaming large datasets from Amazon S3 directly to the training algorithm without downloading them to the local disk. This reduces the startup time, disk space, and cost of training jobs. Pipe input mode is supported by most of the built-in algorithms and can also be used with custom training algorithms. To use Pipe input mode, the data needs to be in a binary format such as protobuf recordIO or TFRecord. The training code needs to use the PipeModeDataset class to read the data from the named pipe provided by SageMaker. To verify that the training code and the model parameters are working as expected, it is recommended to train locally on a smaller subset of the data before launching a full-scale training job on SageMaker. This approach is faster and more efficient than the other options, which involve either downloading the full dataset to an EC2 instance or using AWS Glue, which is not designed for training machine learning models. References:

• Using Pipe input mode for Amazon SageMaker algorithms
• Using Pipe Mode with Your Own Algorithms
• PipeModeDataset Class

The best Amazon SageMaker algorithm for this task is Image Classification - TensorFlow. This algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub. Transfer learning allows the company to fine-tune one of the available pretrained models on their own dataset, even if a large amount of image data is not available. The image classification algorithm takes an image as input and outputs a probability for each provided class label. The company can choose from a variety of models, such as MobileNet, ResNet, or Inception, depending on their accuracy and speed requirements. The algorithm also supports distributed training, data augmentation, and hyperparameter tuning.

References:

• Image Classification - TensorFlow - Amazon SageMaker
• Amazon SageMaker Provides New Built-in TensorFlow Image Classification Algorithm
• Image Classification with ResNet :: Amazon SageMaker Workshop
• Image classification on Amazon SageMaker | by Julien Simon - Medium

Amazon Kinesis Data Firehose is a service that can ingest streaming data and store it in various destinations, including Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. Amazon Kinesis Data Firehose can also convert the incoming data to Apache Parquet or Apache ORC format before storing it in Amazon S3. This can reduce the storage cost and improve the performance of analytical queries on the data. Amazon Kinesis Data Firehose supports various data sources, such as Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka, AWS IoT, and custom applications. Amazon Kinesis Data Firehose can also apply data transformation and compression using AWS Lambda functions.

AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or Parquet format.

Amazon Kinesis Data Streams is a service that can ingest and process streaming data in real time, but it does not store the data in any destination. Amazon Kinesis Data Streams can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format.

Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink, but it does not store the data in any destination. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format. References:

• Amazon Kinesis Data Firehose - Amazon Web Services
• What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose
• Amazon Kinesis Data Firehose FAQs - Amazon Web Services

A scatter plot showing the correlation between maximum tree depth and the objective metric is a visualization that can help the Machine Learning Specialist reconfigure the input hyperparameter range(s) for the tree-based ensemble model. A scatter plot is a type of graph that displays the relationship between two variables using dots, where each dot represents one observation. A scatter plot can show the direction, strength, and shape of the correlation between the variables, as well as any outliers or clusters. In this case, the scatter plot can show how the maximum tree depth, which is a hyperparameter that controls the complexity and depth of the decision trees in the ensemble model, affects the AUC, which is the objective metric that measures the performance of the model in terms of the trade-off between true positive rate and false positive rate. By looking at the scatter plot, the Machine Learning Specialist can see if there is a positive, negative, or no correlation between the maximum tree depth and the AUC, and how strong or weak the correlation is. The Machine Learning Specialist can also see if there is an optimal value or range of values for the maximum tree depth that maximizes the AUC, or if there is a point of diminishing returns or overfitting where increasing the maximum tree depth does not improve or even worsens the AUC. Based on the scatter plot, the Machine Learning Specialist can reconfigure the input hyperparameter range(s) for the maximum tree depth to focus on the values that yield the best AUC, and avoid the values that result in poor AUC. This can decrease the amount of time and cost it takes to train the model, as the hyperparameter tuning job can explore fewer and more promising combinations of values. A scatter plot can be created using various tools and libraries, such as Matplotlib, Seaborn, Plotly, etc12

The other options are not valid or relevant for reconfiguring the input hyperparameter range(s) for the tree-based ensemble model. A histogram showing whether the most important input feature is Gaussian is a visualization that can help the Machine Learning Specialist understand the distribution and shape of the input data, but not the hyperparameters. A histogram is a type of graph that displays the frequency or count of values in a single variable using bars, where each bar represents a bin or interval of values. A histogram can show if the variable is symmetric, skewed, or multimodal, and if it follows a normal or Gaussian distribution, which is a bell-shaped curve that is often assumed by many machine learning algorithms. In this case, the histogram can show if the most important input feature, which is a variable that has the most influence or predictive power on the output variable, is Gaussian or not. However, this does not help the Machine Learning Specialist reconfigure the input hyperparameter range(s) for the tree-based ensemble model, as the input feature is not a hyperparameter that can be tuned or optimized. A histogram can be created using various tools and libraries, such as Matplotlib, Seaborn, Plotly, etc34

A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension is a visualization that can help the Machine Learning Specialist understand the structure and clustering of the input data, but not the hyperparameters. t-SNE is a technique that can reduce the dimensionality of high-dimensional data, such as images, text, or gene expression, and project it onto a lower-dimensional space, such as two or three dimensions, while preserving the local similarities and distances between the data points. t-SNE can help visualize and explore the patterns and relationships in the data, such as the clusters, outliers, or separability of the classes. In this case, the scatter plot can show how the input variables, which are the features or predictors of the output variable, are mapped onto a two-dimensional space using t-SNE, and how the points are colored by the target variable, which is the output or response variable that the model tries to predict. However, this does not help the Machine Learning Specialist reconfigure the input hyperparameter range(s) for the tree-based ensemble model, as the input variables and the target variable are not hyperparameters that can be tuned or optimized. A scatter plot with t-SNE can be created using various tools and libraries, such as Scikit-learn, TensorFlow, PyTorch, etc5

A scatter plot showing the performance of the objective metric over each training iteration is a visualization that can help the Machine Learning Specialist understand the learning curve and convergence of the model, but not the hyperparameters. A scatter plot is a type of graph that displays the relationship between two variables using dots, where each dot represents one observation. A scatter plot can show the direction, strength, and shape of the correlation between the variables, as well as any outliers or clusters. In this case, the scatter plot can show how the objective metric, which is the performance measure that the model tries to optimize, changes over each training iteration, which is the number of times that the model updates its parameters using a batch of data. A scatter plot can show if the objective metric improves, worsens, or stagnates over time, and if the model converges to a stable value or oscillates or diverges. However, this does not help the Machine Learning Specialist reconfigure the input hyperparameter range(s) for the tree-based ensemble model, as the objective metric and the training iteration are not hyperparameters that can be tuned or optimized. A scatter plot can be created using various tools and libraries, such as Matplotlib, Seaborn, Plotly, etc.

• Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.
• Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.
• Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model’s ability to learn from the data.
• Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.
• Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.
• Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model’s convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.

References:

• : Image Augmentation - Amazon SageMaker
• : Amazon Rekognition Custom Labels Features
• : [Handling Imbalanced Datasets in Machine Learning]
• : [Semantic Segmentation - Amazon SageMaker]
• : [DetectLabels - Amazon Rekognition]
• : [Image Classification - MXNet - Amazon SageMaker]
• : [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
• : [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
• : [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
• : [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
• : [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
• : [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
• : [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
• : [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
• : [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
• : [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
• : [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
• : [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

The best approach to address all of the requirements with the least development effort is to create an AWS Glue job, convert the scripts to PySpark, execute the pipeline, and store the results in Amazon S3. This is because:

• AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics 1. AWS Glue can run Python and Scala scripts to process data from various sources, such as Amazon S3, Amazon DynamoDB, Amazon Redshift, and more 2. AWS Glue also provides a serverless Apache Spark environment to run ETL jobs, eliminating the need to provision and manage servers 3.
• PySpark is the Python API for Apache Spark, a unified analytics engine for large-scale data processing 4. PySpark can perform various data transformations and manipulations on structured and unstructured data, such as cleaning, enriching, and compressing 5. PySpark can also leverage the distributed computing power of Spark to handle terabytes of data efficiently and scalably 6.
• By creating an AWS Glue job and converting the scripts to PySpark, the company can move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers. The company can also reduce the development effort by using the AWS Glue console, AWS SDK, or AWS CLI to create and run the job 7. Moreover, the company can use the AWS Glue Data Catalog to store and manage the metadata of the data sources and targets 8.

The other options are not as suitable as option C for the following reasons:

• Option A is not optimal because loading the data into an Amazon Redshift cluster and executing the pipeline by using SQL will incur additional costs and complexity for the company. Amazon Redshift is a fully managed data warehouse service that enables fast and scalable analysis of structured data . However, it is not designed for ETL purposes, such as cleaning, transforming, enriching, and compressing data. Moreover, using SQL to perform these tasks may not be as expressive and flexible as using Python scripts. Furthermore, the company will have to provision and configure the Amazon Redshift cluster, and load and unload the data from Amazon S3, which will increase the development effort and time.
• Option B is not feasible because loading the data into Amazon DynamoDB and converting the scripts to an AWS Lambda function will not work for the company’s use case. Amazon DynamoDB is a fully managed key-value and document database service that provides fast and consistent performance at any scale . However, it is not suitable for storing and processing terabytes of data daily, as it has limits on the size and throughput of each table and item . Moreover, using AWS Lambda to execute the pipeline will not be efficient or cost-effective, as Lambda has limits on the memory, CPU, and execution time of each function . Therefore, using Amazon DynamoDB and AWS Lambda will not meet the company’s requirements for processing large amounts of data quickly and reliably.
• Option D is not relevant because creating a set of individual AWS Lambda functions to execute each of the scripts and building a step function by using the AWS Step Functions Data Science SDK will not address the main issue of moving the scripts out of Amazon EC2. AWS Step Functions is a fully managed service that lets you coordinate multiple AWS services into serverless workflows . The AWS Step Functions Data Science SDK is an open source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions . However, these services and tools are not designed for ETL purposes, such as cleaning, transforming, enriching, and compressing data. Moreover, as mentioned in option B, using AWS Lambda to execute the scripts will not be efficient or cost-effective for the company’s use case.

References:

• What Is AWS Glue?
• AWS Glue Components
• AWS Glue Serverless Spark ETL
• PySpark - Overview
• PySpark - RDD
• PySpark - SparkContext
• Adding Jobs in AWS Glue
• Populating the AWS Glue Data Catalog
• [What Is Amazon Redshift?]
• [What Is Amazon DynamoDB?]
• [Service, Account, and Table Quotas in DynamoDB]
• [AWS Lambda quotas]
• [What Is AWS Step Functions?]
• [AWS Step Functions Data Science SDK for Python]

Amazon Kinesis and AWS Data Pipeline are two services that can feed data to the Amazon EMR MapReduce jobs. Amazon Kinesis is a service that can ingest, process, and analyze streaming data in real time. Amazon Kinesis can be integrated with Amazon EMR to run MapReduce jobs on streaming data sources, such as web logs, social media, IoT devices, and clickstreams. Amazon Kinesis can handle data that needs to be processed in near-real time, such as for anomaly detection, fraud detection, or dashboarding. AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run MapReduce jobs on batch data sources, such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon Redshift. AWS Data Pipeline can handle data that can be moved hourly, such as for data warehousing, reporting, or machine learning.

AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or MapReduce jobs.

Amazon Athena is a service that can query data stored in Amazon S3 using standard SQL, but it does not feed data to Amazon EMR or run MapReduce jobs.

Amazon ES is a service that provides a fully managed Elasticsearch cluster, which can be used for search, analytics, and visualization, but it does not feed data to Amazon EMR or run MapReduce jobs. References:

• Using Amazon Kinesis with Amazon EMR - Amazon EMR
• AWS Data Pipeline - Amazon Web Services
• Using AWS Data Pipeline to Run Amazon EMR Jobs - AWS Data Pipeline

The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) – answers that need to be predicted – to train an algorithm. With classification, businesses can answer the following questions:

• Will this customer churn or not?
• Will a customer renew their subscription?
• Will a user downgrade a pricing plan?
• Are there any signs of unusual customer behavior?

The solution C will create a new dataset with the added features with the least operational overhead because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution C involves the following steps:

• Create a new flow in Amazon SageMaker Data Wrangler. A flow is a visual representation of the data preparation steps that can be applied to one or more datasets. The data scientist can create a new flow in the Amazon SageMaker Studio interface and import the S3 file as a data source1.
• Use the Featurize date/time transform to generate the new variables. Amazon SageMaker Data Wrangler provides a set of preconfigured transformations that can be applied to the data with a few clicks. The Featurize date/time transform can parse a date/time column and generate new columns for the year, month, day, hour, minute, second, day of week, and day of year. The data scientist can use this transform to create the new variables from the timestamp variable2.
• Save the dataset as a new file in Amazon S3. Amazon SageMaker Data Wrangler can export the transformed dataset as a new file in Amazon S3, or as a feature store in Amazon SageMaker Feature Store. The data scientist can choose the output format and location of the new file3.

The other options are not suitable because:

• Option A: Creating an Amazon EMR cluster and developing PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the Amazon EMR cluster, the PySpark application, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing4.
• Option B: Creating a processing job in Amazon SageMaker and developing Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the processing job, the Python code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing5.
• Option D: Creating an AWS Glue job and developing code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the AWS Glue job, the code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing6.

References:

• 1: Amazon SageMaker Data Wrangler
• 2: Featurize Date/Time - Amazon SageMaker Data Wrangler
• 3: Exporting Data - Amazon SageMaker Data Wrangler
• 4: Amazon EMR
• 5: Processing Jobs - Amazon SageMaker
• 6: AWS Glue

• B. Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.
• C. Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other. This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: =
• Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.
• Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.

 The best solution for building a line-counting application for use in a quick-service restaurant is to use the following steps:

• Build a custom model in Amazon SageMaker to recognize the number of people in an image. Amazon SageMaker is a fully managed service that provides tools and workflows for building, training, and deploying machine learning models. A custom model can be tailored to the specific use case of line-counting and achieve higher accuracy than a generic model1
• Deploy AWS DeepLens cameras in the restaurant to capture video. AWS DeepLens is a wireless video camera that integrates with Amazon SageMaker and AWS Lambda. It can run machine learning inference locally on the device without requiring internet connectivity or streaming video to the cloud. This reduces the bandwidth consumption and latency of the application2
• Deploy the model to the cameras. AWS DeepLens allows users to deploy trained models from Amazon SageMaker to the cameras with a few clicks. The cameras can then use the model to process the video frames and count the number of people in each frame2
• Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. AWS Lambda is a serverless computing service that lets users run code without provisioning or managing servers. AWS DeepLens supports running Lambda functions on the device to perform actions based on the inference results. Amazon SNS is a service that enables users to send notifications to subscribers via email, SMS, or mobile push23

The other options are incorrect because they either require internet connectivity or streaming video to the cloud, which may impact the bandwidth and performance of the application. For example:

• Option A uses Amazon Kinesis Video Streams to stream the data to AWS over the restaurant’s existing internet connection. Amazon Kinesis Video Streams is a service that enables users to capture, process, and store video streams for analytics and machine learning. However, this option requires streaming multiple video streams to the cloud, which may consume a lot of bandwidth and cause network congestion. It also requires internet connectivity, which may not be reliable or available in some locations4
• Option B uses Amazon Rekognition on the AWS DeepLens device. Amazon Rekognition is a service that provides computer vision capabilities, such as face detection, face recognition, and object detection. However, this option requires calling the Amazon Rekognition API over the internet, which may introduce latency and require bandwidth. It also uses a generic face detection model, which may not be optimized for the line-counting use case.
• Option C uses Amazon SageMaker to build a custom model and an Amazon SageMaker endpoint to call the model. Amazon SageMaker endpoints are hosted web services that allow users to perform inference on their models. However, this option requires sending the images to the endpoint over the internet, which may consume bandwidth and introduce latency. It also requires internet connectivity, which may not be reliable or available in some locations.

References:

• 1: Amazon SageMaker – Machine Learning Service - AWS
• 2: AWS DeepLens - Deep learning enabled video camera - AWS
• 3: Amazon Simple Notification Service (SNS) - AWS
• 4: Amazon Kinesis Video Streams - Amazon Web Services
• : Amazon Rekognition – Video and Image - AWS
• : Deploy a Model - Amazon SageMaker

The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user’s identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.

References:

• AWS Machine Learning Specialty Exam Guide
• AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3
• AWS Machine Learning Training - Using Bucket Policies and User Policies

The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize. Some of these methods are:

• Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.
• Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.
• Decrease feature combinations: Feature combinations are the interactions between different input features that can be used to create new features for the model. However, too many feature combinations can increase the complexity of the model and cause overfitting. Therefore, the Specialist should decrease the number of feature combinations and select only the most relevant and informative ones for the model3.

References:

• 1: Regularization for Deep Learning - Amazon SageMaker
• 2: Dropout - Amazon SageMaker
• 3: Feature Engineering - Amazon SageMaker

The services that are integrated with Amazon SageMaker to track the information that the Machine Learning Specialist needs are AWS CloudTrail and Amazon CloudWatch. AWS CloudTrail is a service that records the API calls and events for AWS services, including Amazon SageMaker. AWS CloudTrail can track the actions performed by the Data Scientists, such as creating notebooks, training models, and deploying endpoints. AWS CloudTrail can also provide information such as the identity of the user, the time of the action, the parameters used, and the response elements returned. AWS CloudTrail can help the Machine Learning Specialist to monitor the usage and activity of Amazon SageMaker, as well as to audit and troubleshoot any issues1 Amazon CloudWatch is a service that collects and analyzes the metrics and logs for AWS services, including Amazon SageMaker. Amazon CloudWatch can track the performance and utilization of the Amazon SageMaker endpoints, such as the CPU and GPU utilization, the inference latency, the number of invocations, etc. Amazon CloudWatch can also track the errors and alarms that are generated when an endpoint is invoked, such as the model errors, the throttling errors, the HTTP errors, etc. Amazon CloudWatch can help the Machine Learning Specialist to optimize the operational performance and reliability of Amazon SageMaker, as well as to set up notifications and actions based on the metrics and logs

Feature selection is the process of reducing the number of input variables to those that are most relevant for predicting the target variable. One way to do this is to run a correlation check of all features against the target variable and remove features with low target variable correlation scores. This means that these features have little or no linear relationship with the target variable and are not useful for the prediction. This can reduce the model’s complexity and improve its performance. References:

• Feature engineering - Machine Learning Lens
• Feature Selection For Machine Learning in Python

An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.

References:

• What is a data lake? - Amazon Web Services
• Amazon S3 Storage Classes - Amazon Simple Storage Service
• Managing your storage lifecycle - Amazon Simple Storage Service
• Best Practices Design Patterns: Optimizing Amazon S3 Performance

AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.

Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low-latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.

References:

• AWS Greengrass Machine Learning Inference - Amazon Web Services
• Machine learning components - AWS IoT Greengrass
• What is AWS Greengrass? | AWS IoT Core | Onica
• GitHub - aws-samples/aws-greengrass-ml-deployment-sample
• AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.

References:

• One-Hot Encoding - Amazon SageMaker
• One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt …
• One-Hot Encoding in Machine Learning | by Nishant Malik | Towards …

 The best way to address the overfitting problem in image classification is to increase the dropout rate at the flatten layer because the model is not generalized enough. Dropout is a regularization technique that randomly drops out some units from the neural network during training, reducing the co-adaptation of features and preventing overfitting. The flatten layer is the layer that converts the output of the convolutional layers into a one-dimensional vector that can be fed into the dense layers. Increasing the dropout rate at the flatten layer means that more features from the convolutional layers will be ignored, forcing the model to learn more robust and generalizable representations from the remaining features.

The other options are not correct for this scenario because:

• Increasing the learning rate would not help with the overfitting problem, as it would make the optimization process more unstable and prone to overshooting the global minimum. A high learning rate can also cause the model to diverge or oscillate around the optimal solution, resulting in poor performance and accuracy.
• Increasing the dimensionality of the dense layer next to the flatten layer would not help with the overfitting problem, as it would make the model more complex and increase the number of parameters to be learned. A more complex model can fit the training data better, but it can also memorize the noise and irrelevant details in the data, leading to overfitting and poor generalization.
• Increasing the epoch number would not help with the overfitting problem, as it would make the model train longer and more likely to overfit the training data. A high epoch number can cause the model to converge to the global minimum, but it can also cause the model to over-optimize the training data and lose the ability to generalize to new data.

References:

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting
• How to Reduce Overfitting With Dropout Regularization in Keras
• How to Control the Stability of Training Neural Networks With the Learning Rate
• How to Choose the Number of Hidden Layers and Nodes in a Feedforward Neural Network?
• How to decide the optimal number of epochs to train a neural network?

The SageMaker Spark library is a library that enables Apache Spark applications to integrate with Amazon SageMaker for training and hosting machine learning models. The library provides several features, such as:

• Estimators: Classes that allow Spark users to train Amazon SageMaker models and host them on Amazon SageMaker endpoints using the Spark MLlib Pipelines API. The library supports various built-in algorithms, such as linear learner, XGBoost, K-means, etc., as well as custom algorithms using Docker containers.
• Model classes: Classes that wrap Amazon SageMaker models in a Spark MLlib Model abstraction. This allows Spark users to use Amazon SageMaker endpoints for inference within Spark applications.
• Data sources: Classes that allow Spark users to read data from Amazon S3 using the Spark Data Sources API. The library supports various data formats, such as CSV, LibSVM, RecordIO, etc.

To integrate the Spark application with SageMaker, the Machine Learning Specialist should do the following:

• Install the SageMaker Spark library in the Spark environment. This can be done by using Maven, pip, or downloading the JAR file from GitHub.
• Use the appropriate estimator from the SageMaker Spark Library to train a model. For example, to train a linear learner model, the Specialist can use the following code:

• Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker. For example, to get predictions for a test DataFrame, the Specialist can use the following code:

References:

• [SageMaker Spark]: A documentation page that introduces the SageMaker Spark library and its features.
• [SageMaker Spark GitHub Repository]: A GitHub repository that contains the source code, examples, and installation instructions for the SageMaker Spark library.

The combination of steps that the ML engineer should take to train the model are to create a .lst file that contains a list of image files and corresponding class labels, upload the .lst file to Amazon S3, and initiate transfer learning by training the model using the images of less common species. This approach will allow the ML engineer to leverage the existing ImageNetV2 CNN model and fine-tune it with the new data using Pipe mode in SageMaker.

A .lst file is a text file that contains a list of image files and corresponding class labels, separated by tabs. The .lst file format is required for using the SageMaker image classification algorithm with Pipe mode. Pipe mode is a feature of SageMaker that enables streaming data directly from Amazon S3 to the training instances, without downloading the data first. Pipe mode can reduce the startup time, improve the I/O throughput, and enable training on large datasets that exceed the disk size limit. To use Pipe mode, the ML engineer needs to upload the .lst file to Amazon S3 and specify the S3 path as the input data channel for the training job1.

Transfer learning is a technique that enables reusing a pre-trained model for a new task by fine-tuning the model parameters with new data. Transfer learning can save time and computational resources, as well as improve the performance of the model, especially when the new task is similar to the original task. The SageMaker image classification algorithm supports transfer learning by allowing the ML engineer to specify the number of output classes and the number of layers to be retrained. The ML engineer can use the existing ImageNetV2 CNN model, which is trained on 1,000 classes of common objects, and fine-tune it with the new data of less common animal species, which is a similar task2.

The other options are either less effective or not supported by the SageMaker image classification algorithm. Using a ResNet model and initiating full training mode would require training the model from scratch, which would take more time and resources than transfer learning. Using an Inception model is not possible, as the SageMaker image classification algorithm only supports ResNet and ImageNetV2 models. Using an augmented manifest file in JSON Lines format is not compatible with Pipe mode, as Pipe mode only supports .lst files for image classification1.

References:

• 1: Using Pipe input mode for Amazon SageMaker algorithms | AWS Machine Learning Blog
• 2: Image Classification Algorithm - Amazon SageMaker

The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics

According to the Amazon Kinesis Data Streams documentation, the maximum size of data blob (the data payload before Base64-encoding) per record is 1 MB. The maximum number of records that can be sent to a shard per second is 1,000. Therefore, the maximum throughput of a shard is 1 MB/sec for input and 2 MB/sec for output. In this case, the input throughput is 100 transactions per second * 100 KB per transaction = 10 MB/sec. Therefore, the minimum number of shards required is 10 MB/sec / 1 MB/sec = 10 shards. However, the question asks for the minimum number of shards in Kinesis Data Streams, not the minimum number of shards per stream. A Kinesis Data Streams account can have multiple streams, each with its own number of shards. Therefore, the minimum number of shards in Kinesis Data Streams is 1, which is the minimum number of shards per stream. References:

• Amazon Kinesis Data Streams Terminology and Concepts
• Amazon Kinesis Data Streams Limits

Amazon Kinesis Data Firehose is a fully managed service that can capture, transform, and load streaming data into AWS data stores, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. It can also invoke AWS Lambda functions to perform custom transformations on the data. Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. RCF is an unsupervised learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. By using Kinesis Data Firehose and Kinesis Data Analytics, the cybersecurity company can ingest the data in real time, score the malicious events as anomalies, and stream the results to Amazon S3, which can serve as a data lake for later processing and analysis. This is the most efficient way to accomplish these tasks, as it does not require any additional infrastructure, coding, or training.

References:

• Amazon Kinesis Data Firehose - Amazon Web Services
• Amazon Kinesis Data Analytics - Amazon Web Services
• Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services
• [AWS Certified Machine Learning - Specialty Sample Questions]

Normalization is a data preprocessing technique that can be used to scale the input features to a common range, such as [-1, 1] or [0, 1]. Normalization can help reduce the effect of outliers, improve the convergence of gradient-based algorithms, and prevent variables with a larger magnitude from dominating the model. One common method of normalization is standardization, which transforms each feature to have a mean of 0 and a variance of 1. This can be done by subtracting the mean and dividing by the standard deviation of each feature. Standardization can be useful for models that assume the input features are normally distributed, such as linear regression, logistic regression, and support vector machines. References:

• Data normalization and standardization: A video that explains the concept and benefits of data normalization and standardization.
• Standardize or Normalize?: A blog post that compares different methods of scaling the input features.

The data scientist should remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library, and replace the blog post data in the S3 bucket with the results of the vectorizer. This is because:

• The Count Vectorizer function is a tool that can convert a collection of text documents to a matrix of token counts 1. It also enables the pre-processing of text data prior to generating the vector representation, such as removing accents, converting to lowercase, and filtering out stop words 1. By using this function, the data scientist can remove the stop words such as “a,” “an,” and “the” from the blog post data, and obtain a numerical representation of the text that can be used as input for the NTM algorithm.
• The NTM algorithm is a neural network-based topic modeling technique that can learn latent topics from a corpus of documents 2. It can be used to recommend tags from blog posts by finding the most probable topics for each document, and ranking the words associated with each topic 3. However, the NTM algorithm does not perform any text pre-processing by itself, so it relies on the quality of the input data. Therefore, the data scientist should replace the blog post data in the S3 bucket with the results of the vectorizer, to ensure that the NTM algorithm does not include the stop words in the tag recommendations.
• The other options are not suitable for the following reasons:

References:

• Neural Topic Model (NTM) Algorithm
• Introduction to the Amazon SageMaker Neural Topic Model
• Amazon Comprehend - Entity Recognition
• sklearn.feature_extraction.text.CountVectorizer
• Principal Component Analysis (PCA) Algorithm
• Object Detection Algorithm

 Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model. References:

• AWS Machine Learning Specialty Exam Guide
• AWS Machine Learning Specialty Sample Questions

 To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images. This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.

References:

• 1: Image Augmentation - Amazon SageMaker

• To develop a daily ETL workflow containing multiple ETL jobs that can start as soon as data is uploaded to Amazon S3, the best configuration is to use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
• AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that respond to events such as data uploads to Amazon S3. You can also use Lambda to invoke other AWS services such as AWS Step Functions and AWS Glue.
• AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use Step Functions to create a state machine that defines the sequence and logic of your ETL workflow. You can also use Step Functions to handle errors and retries, and to monitor the execution status of your workflow.
• AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics. You can use Glue to create and run ETL jobs that can join data from multiple sources in Amazon S3. You can also use Glue to catalog your data and make it searchable and queryable.
• Amazon CloudWatch is a service that monitors your AWS resources and applications. You can use CloudWatch to create alarms that trigger actions when a metric or a log event meets a specified threshold. You can also use CloudWatch to send notifications to Amazon Simple Notification Service (SNS) topics, which can then deliver the notifications to subscribers such as email addresses or phone numbers.
• Therefore, by using these services together, you can achieve the following benefits:

Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.

Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.

References:

• Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library
• Time Series Forecasting Service – Amazon Forecast – Amazon Web Services
• Amazon Forecast: Guide to Predicting Future Outcomes - Onica
• Amazon Launches What-If Analyses for Machine Learning Forecasting …

The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1

The other options are incorrect because they either do not update the software or require unnecessary steps. For example:

• Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2
• Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3
• Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4

References:

• 1: Amazon SageMaker Notebook Instances - Amazon SageMaker
• 2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
• 3: Create a Notebook Instance - Amazon SageMaker
• 4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker

The approach that will meet the requirements with the least operational overhead is to deploy all the models to a single SageMaker endpoint, treat each model as a production variant, configure an S3 event notification that invokes an AWS Lambda function when new documents are created, and configure the Lambda function to call each production variant and return the results of each model. This approach involves the following steps:

• Deploy all the models to a single SageMaker endpoint. Amazon SageMaker is a service that can build, train, and deploy machine learning models. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.
• Treat each model as a production variant. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.
• Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Amazon S3 is a service that can store and retrieve any amount of data. Amazon S3 can send event notifications when certain actions occur on the objects in a bucket, such as object creation, deletion, or modification. Amazon S3 can invoke an AWS Lambda function as a destination for the event notifications. AWS Lambda is a service that can run code without provisioning or managing servers2.
• Configure the Lambda function to call each production variant and return the results of each model. AWS Lambda can execute the code that can call the SageMaker endpoint and specify the production variant to invoke. AWS Lambda can use the AWS SDK or the SageMaker Runtime API to send requests to the endpoint and receive the predictions from the models. AWS Lambda can return the results of each model as a response to the event notification3.

The other options are not suitable because:

• Option A: Configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document, will incur more operational overhead than using a single SageMaker endpoint. Amazon SageMaker batch transform is a service that can process large datasets in batches and store the predictions in Amazon S3. Amazon SageMaker batch transform is not suitable for real-time inference, as it introduces a delay between the request and the response. Moreover, creating three batch transform jobs for each document will increase the complexity and cost of the solution4.
• Option C: Deploying each model to its own SageMaker endpoint, configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to call each endpoint and return the results of each model, will incur more operational overhead than using a single SageMaker endpoint. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Moreover, calling each endpoint separately will increase the latency and network traffic of the solution5.
• Option D: Deploying each model to its own SageMaker endpoint, creating three AWS Lambda functions, configuring each Lambda function to call a different endpoint and return the results, configuring three S3 event notifications to invoke the Lambda functions when new documents are created, will incur more operational overhead than using a single SageMaker endpoint and a single Lambda function. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Creating three Lambda functions will increase the complexity and cost of the solution. Configuring three S3 event notifications will increase the number of triggers and destinations to manage and monitor6.

References:

• 1: Deploying Multiple Models to a Single Endpoint - Amazon SageMaker
• 2: Configuring Amazon S3 Event Notifications - Amazon Simple Storage Service
• 3: Invoke an Endpoint - Amazon SageMaker
• 4: Get Inferences for an Entire Dataset with Batch Transform - Amazon SageMaker
• 5: Deploy a Model - Amazon SageMaker
• 6: AWS Lambda

• Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model’s performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.
• Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model’s performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.
• Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets. MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.
• Option C is incorrect because setting the predictor type to regressor would change the model’s objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.
• Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.

References:

• Amazon SageMaker Linear Learner Algorithm
• Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm
• [Principal Component Analysis - Scikit-learn]
• [Multidimensional Scaling - Scikit-learn]

The best solution to improve the accuracy of the model and receive notifications for any future performance issues is to perform incremental training to update the model and activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Incremental training is a technique that allows you to update an existing model with new data without retraining the entire model from scratch. This can save time and resources, and help the model adapt to changing data patterns. Amazon SageMaker Model Monitor is a feature that continuously monitors the quality of machine learning models in production and notifies you when there are deviations in the model quality, such as data drift and anomalies. You can set up alerts that trigger actions, such as sending notifications to Amazon Simple Notification Service (Amazon SNS) topics, when certain conditions are met.

Option B is incorrect because Amazon SageMaker Model Governance is a set of tools that help you implement ML responsibly by simplifying access control and enhancing transparency. It does not provide a mechanism to automatically adjust model hyperparameters or improve model accuracy.

Option C is incorrect because Amazon SageMaker Debugger is a feature that helps you debug and optimize your model training process by capturing relevant data and providing real-time analysis. However, using Debugger alone does not update the model or monitor its performance in production. Also, retraining the model by using only data from the previous several months may not capture the full range of data variability and may introduce bias or overfitting.

Option D is incorrect because using only data from the previous several months to perform incremental training may not be sufficient to improve the model accuracy, as explained above. Moreover, this option does not specify how to activate Amazon SageMaker Model Monitor or configure the alerts and notifications.

References:

• Incremental training
• Amazon SageMaker Model Monitor
• Amazon SageMaker Model Governance
• Amazon SageMaker Debugger

 The XGBoost model is a supervised machine learning algorithm, which means it requires labeled data to learn from. The customers’ current segmentation is unknown, so there is no label to train the XGBoost model on. Moreover, the XGBoost model is designed for classification or regression tasks, not for clustering. Clustering is a type of unsupervised machine learning, which means it does not require labeled data. Clustering algorithms try to find natural groups or clusters in the data based on their similarity or distance. A common clustering algorithm is k-means, which partitions the data into K clusters, where each data point belongs to the cluster with the nearest mean. To meet the current requirements, the data scientist should train a k-means model with K = 5 on the same dataset to predict a segment for each customer. This way, the data scientist can identify five customer segments based on age, income, and location, without needing any labels. References:

• What is XGBoost? - Amazon SageMaker
• What is Clustering? - Amazon SageMaker
• K-Means Algorithm - Amazon SageMaker

Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options.

References:

• Amazon SageMaker Data Wrangler - Amazon Web Services (AWS)
• Anomaly Detection Visualization - Amazon SageMaker
• Transform Data - Amazon SageMaker