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 MLS-C01 Dumps with Practice Exam Questions Answers

Questions: 208 questions

Last Update: Jan 17, 2022

Amazon Web Services Certification Exam MLS-C01 has been designed to measure your skills in handling the technical tasks mentioned in the certification syllabus

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MLS-C01 Questions and Answers

Question # 1

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

A.

Root Mean Square Error (RMSE)

B.

Residual plots

C.

Area under the curve

D.

Confusion matrix

Question # 2

A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.

Which solution should a machine learning specialist implement to meet these requirements?

A.

Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

B.

Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

C.

Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

D.

Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. 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.

Question # 3

A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:

* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.

* Support event-driven ETL pipelines.

* Provide a quick and easy way to understand metadata.

Which approach meets trfese requirements?

A.

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

B.

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

C.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

D.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Question # 4

A company wants to predict the sale prices of houses based on available historical sales data. The target

variable in the company’s dataset is the sale price. The features include parameters such as the lot size, living

area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built,

and postal code. The company wants to use multi-variable linear regression to predict house sale prices.

Which step should a machine learning specialist take to remove features that are irrelevant for the analysis

and reduce the model’s complexity?

A.

Plot a histogram of the features and compute their standard deviation. Remove features with high variance.

B.

Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

C.

Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual

correlation scores.

D.

Run a correlation check of all features against the target variable. Remove features with low target variable

correlation scores.

Question # 5

A machine learning specialist works for a fruit processing company and needs to build a system that

categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each

type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this

dataset.

The company requires at least 85% accuracy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68% accuracy on the training set

67% accuracy on the validation set

What can the machine learning specialist do to improve the system’s accuracy?

A.

Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO

feature to optimize the model’s hyperparameters.

B.

Add more data to the training set and retrain the model using transfer learning to reduce the bias.

C.

Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning

to increase the variance.

D.

Train a new model using the current neural network architecture.

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