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MLA-C02 practice question: Training with SageMaker AI (2 of 3)

ML Model and Foundation Model (FM) Development A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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An esports league trains a video highlight model every night on a GPU instance in SageMaker AI. The run takes about five hours, and the team wants to cut its training bill without losing more than a few minutes of progress when capacity is reclaimed.

The training script already saves a checkpoint every few minutes and can resume from the latest one. The model output must still land in the team's S3 bucket.

Which configuration meets these requirements?

Choose one answer.

  1. A. Use On-Demand instances for the job, and purchase a Compute Savings Plan that covers the account's Amazon EC2 usage.
  2. B. Enable managed Spot training without a checkpoint location, so that an interrupted job restarts from the first epoch.
  3. C. Enable managed Spot training, and configure a checkpoint location in S3 so that the job restores from its last checkpoint after an interruption.
  4. D. Halve the number of epochs to shorten each run, and keep the job on On-Demand capacity at the current instance size.
Show the answer and explanation

Answer: C

  • A. Wrong: Compute Savings Plans apply to Amazon EC2, AWS Fargate and AWS Lambda usage, not to SageMaker AI training instances, so the training bill would not fall. SageMaker AI has its own Savings Plans, and the option still pays On-Demand rates for the job.
  • B. Wrong: Without a checkpoint location, an interrupted job loses all its progress and must start over, which can cost more than Spot saves. The stem allows only a few minutes of lost progress.
  • C. Right: Correct. Managed Spot training lowers the cost of the instances, and checkpoints saved to S3 let SageMaker AI restore the job from its last saved state. Only the work since the last checkpoint is repeated. Learn more: Managed Spot Training in Amazon SageMaker AI (opens in a new tab)
  • D. Wrong: Fewer epochs may hurt model quality and do not reduce the instance cost. The stem asks to lower cost while keeping the training progress.

Explanation

Managed Spot training uses discounted spare capacity, and with a checkpoint location in S3 SageMaker AI restores the job from its latest checkpoint after an interruption. Compute Savings Plans do not cover SageMaker AI usage, and skipping checkpoints or cutting epochs does not meet the requirement.

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