QUESTION: 5

A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Answer(s): D

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QUESTION: 6

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 instance within the VPC.

Why is the ML Specialist not seeing the instance visible in the VPC?

Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

Answer(s): C

Reference:

https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html

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QUESTION: 7

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

Answer(s): B

Reference:

https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html

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QUESTION: 8

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.

Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
Use AWS Glue to catalogue the data and Amazon Athena to run queries.
Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Answer(s): B

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QUESTION: 5

QUESTION: 6

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QUESTION: 7

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QUESTION: 8