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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by cezar

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 322 Q&A's Shared By: cezar
Question 28

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?

Options:

A.

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

B.

Use AWS Glue to catalogue the data and Amazon Athena to run queries.

C.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

D.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Discussion
Question 29

A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.

Which solution will meet these requirements?

Options:

A.

Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.

B.

Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.

C.

Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the 1AM role.

D.

Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.

Discussion
Question 30

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?

Options:

A.

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMakerinstance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

B.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automaticallydiscard credit card numbers and insert fake credit card numbers.

C.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMakerinstance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the lengthof the credit card numbers.

D.

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.

Discussion
Question 31

A technology startup is using complex deep neural networks and GPU compute to recommend the company’s products to its existing customers based upon each customer’s habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company’s Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.

Senior managers are concerned about the complexity of the solution’s resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.

Which architecture should be used to scale the solution at the lowest cost?

Options:

A.

Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance

B.

Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task

C.

Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler

D.

Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler

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