Amazon MLS-C01 AWS ML Specialty Exam Practice Test Instant Access

Question 1

[Modeling]

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?

AInstall 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.

BDeploy 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.

CBuild 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.

DBuild 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.

Answer : D

Question 2

[Modeling]

A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.

After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test dat

a. The ML specialist needs to improve the accuracy of the model.

Which solutions will meet this requirement? (Select TWO.)

AIncrease the number of passes on the existing training data. Perform more hyperparameter tuning.

BIncrease the amount of regularization. Use fewer feature combinations.

CAdd new domain-specific features. Use more complex models.

DUse fewer feature combinations. Decrease the number of numeric attribute bins.

EDecrease the amount of training data examples. Reduce the number of passes on the existing training data.

Answer : A, C

Question 3

[Data Engineering]

A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.

A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.

Which AWS service or feature will meet these requirements with the LEAST development effort over time?

AAWS Glue jobs

BAmazon EMR cluster

CAmazon Athena

DAWS Lambda

Answer : A

Question 4

[Modeling]

A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadat

a. The solution also must be able to set the data sensitivity and authorship of features.

Which solution will meet these requirements with the LEAST development effort?

AUse Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

BUse Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.

CUse Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

DUse Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

Answer : D

Question 5

[Modeling]

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.

Which solution will meet these requirements with the LEAST development effort?

AUse SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

BUse AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.

CUse SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

DUse custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.

Answer : C

Question 6

[Data Engineering]

A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products.

Which solution will meet these requirements with the MOST operational efficiency?

ABuild a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.

BTokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.

CTrain an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

DTrain an Amazon SageMaker Blazing Text model to generate the product categories.

Answer : C

Question 7

[Data Engineering]

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3

The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3

Which solution takes the LEAST effort to implement?

AIngest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 toserialize data as Parquet

BIngest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

CIngest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use ApacheSpark to convert data into Parquet.

DIngest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convertdata into Parquet.

Answer : D

Amazon AWS Certified Machine Learning - Specialty MLS-C01 AWS ML Specialty Exam Practice Test