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MLS-C01 Questions and Answers

Question # 6

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.

Which steps would improve the accuracy of the solution? (Choose three.)

A.

Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.

B.

Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.

C.

Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.

D.

Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

E.

Use Amazon Rekognition Custom Labels to train a new model.

F.

Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

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Question # 7

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

A.

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

B.

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

C.

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

D.

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

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Question # 8

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS

Which approach should the Specialist use for training a model using that data?

A.

Write a direct connection to the SQL database within the notebook and pull data in

B.

Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

C.

Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in

D.

Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

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Question # 9

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers’ current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.

Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

A.

The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

B.

The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

C.

The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

D.

The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

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Question # 10

A Machine Learning Specialist works for a credit card processing company and needs to predict which

transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the

probability that a given transaction may fraudulent.

How should the Specialist frame this business problem?

A.

Streaming classification

B.

Binary classification

C.

Multi-category classification

D.

Regression classification

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Question # 11

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing

rapidly and approximately 100 GB of new data is generated every day. The company wants to explore

machine learning uses cases while ensuring the data is only accessible to specific IAM users.

Which storage option provides the most processing flexibility and will allow access control with IAM?

A.

Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict

access to only the desired IAM users.

B.

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket

policies.

C.

Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access

to the EMR instances using IAM policies.

D.

Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by

the IAM users.

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Question # 12

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?

A.

Create one-hot word encoding vectors.

B.

Produce a set of synonyms for every word using Amazon Mechanical Turk.

C.

Create word embedding factors that store edit distance with every other word.

D.

Download word embedding’s pre-trained on a large corpus.

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Question # 13

A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.

How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A.

Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

B.

Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.

C.

Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

D.

Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

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Question # 14

An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.

Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A.

Listwise deletion

B.

Last observation carried forward

C.

Multiple imputation

D.

Mean substitution

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Question # 15

A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.

A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.

What is the MOST direct approach to solve this problem within 2 days?

A.

Train a custom classifier by using Amazon Comprehend.

B.

Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.

C.

Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.

D.

Use a built-in seq2seq model in Amazon SageMaker.

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Question # 16

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet this requirements?

A.

Call the CreateNotebookInstanceLifecycleConfig API operation

B.

Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

C.

Stop and then restart the SageMaker notebook instance

D.

Call the UpdateNotebookInstanceLifecycleConfig API operation

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Question # 17

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data

Which type of data repository is the MOST cost-effective solution?

A.

An Amazon EBS-backed Amazon EC2 instance with hourly directories

B.

An Amazon RDS database with hourly table partitions

C.

An Amazon S3 data lake with hourly object prefixes

D.

An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

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Question # 18

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

A.

Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

B.

Logistic regression

C.

Support vector machine (SVM) with non-linear kernel

D.

Single perceptron with tanh activation function

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Question # 19

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A.

Redeploy the model as a batch transform job on an M5 instance.

B.

Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C.

Redeploy the model on a P3dn instance.

D.

Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

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Question # 20

A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.

Which set of actions should the ML specialist take to meet these requirements?

A.

Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.

B.

Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. 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.

C.

Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.

D.

Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.

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Question # 21

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

A.

Recall

B.

Misclassification rate

C.

Mean absolute percentage error (MAPE)

D.

Area Under the ROC Curve (AUC)

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Question # 22

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local

machine, and the Specialist now wants to deploy it to production for inference only.

What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

A.

Build the Docker image with the inference code. Tag the Docker image with the registry hostname and

upload it to Amazon ECR.

B.

Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the

registry hostname and upload it to Amazon S3.

C.

Serialize the trained model so the format is compressed for deployment. Build the image and upload it to

Docker Hub.

D.

Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon

ECR.

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Question # 23

A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.

Which model should be used for categorizing new products using the provided dataset for training?

A.

An XGBoost model where the objective parameter is set to multi: softmax

B.

A deep convolutional neural network (CNN) with a softmax activation function for the last layer

C.

A regression forest where the number of trees is set equal to the number of product categories

D.

A DeepAR forecasting model based on a recurrent neural network (RNN)

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Question # 24

A data science team is planning to build a natural language processing (NLP) application. The application’s text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.

Which solution can the team build MOST quickly to meet these requirements?

A.

Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.

B.

Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

C.

Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.

D.

Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

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Question # 25

A company offers an online shopping service to its customers. The company wants to enhance the site’s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.

The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.

Which approach should an ML specialist take to implement the new security feature in the web application?

A.

Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.

B.

Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.

C.

Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

D.

Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.

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Question # 26

A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions

What metric is BEST suited to score the model?

A.

Precision

B.

Recall

C.

Area Under the ROC Curve (AUC)

D.

Root Mean Square Error (RMSE)

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Question # 27

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

A.

Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.

B.

Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C.

Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D.

Randomly select 10% of the users. Split off all interaction data from these users for the test set.

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Question # 28

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

A.

Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

B.

Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

C.

Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.

D.

Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

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Question # 29

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an,” and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.

What should the data scientist do to meet these requirements?

A.

Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.

B.

Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.

C.

Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.

D.

Remove the stopwords from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.

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Question # 30

A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available

Which model is MOST likely to provide the best results in Amazon SageMaker?

A.

Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of

the full year of data with a predictor_type of regressor.

B.

Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of

data.

C.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year

of data with a predictor_type of regressor.

D.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year

of data with a predictor_type of classifier.

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Question # 31

A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.

The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:

• Real-time analytics

• Interactive analytics of historical data

• Clickstream analytics

• Product recommendations

Which services should the Specialist use?

A.

AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

B.

Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations

C.

AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

D.

Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

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