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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps (page: 6)

Page 5 of 22
PDF with 81 Questions

A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

  1. Use Amazon Mechanical Turk jobs to detect duplicates.
  2. Use Amazon QuickSight ML Insights to build a custom deduplication model.
  3. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
  4. Use the AWS Glue FindMatches transform to detect duplicates.

Answer(s): D



A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

  1. Spot Instances
  2. Reserved Instances
  3. On-Demand Instances
  4. Dedicated Instances

Answer(s): A



An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account В in the same Region.
Which solution will meet this requirement with the LEAST development effort?

  1. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
  2. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account
  3. Use AWS DataSync to replicate the model from Account A to Account B.
  4. Create an AWS Site-to-Site VPN connection between Account A and Account В to transfer the model.

Answer(s): B



An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

  1. Enable early stopping on the model.
  2. Increase dropout in the layers.
  3. Increase the number of layers.
  4. Increase the number of neurons.
  5. Investigate and reduce the sources of model bias.

Answer(s): A,B






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