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Free AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 – Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question

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Showing 10–12 of 24 questions

Question 10 (Topic 1)

A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups. The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker. Which solution will provide the ML engineers with the appropriate access?

Select an option, then click Submit answer.

  • ○ Enable S3 bucket versioning.
  • ○ Configure S3 Object Lock settings for each user.
  • ○ Add cross-origin resource sharing (CORS) policies to the S3 buckets.
  • ○ Create IAM policies. Attach the policies to IAM users or IAM roles.
Question 11 (Topic 1)

An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances. What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Select an option, then click Submit answer.

  • ○ Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
  • ○ Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
  • ○ Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
  • ○ Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
Question 12 (Topic 1)

A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model. Which solution will meet these requirements?

Select an option, then click Submit answer.

  • ○ Use Amazon Made to categorize the sensitive data.
  • ○ Prepare the data by using AWS Glue DataBrew.
  • ○ Run an AWS Batch job to change the sensitive data to random values.
  • ○ Run an Amazon EMR job to change the sensitive data to random values.