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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 110 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 110
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation.

The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time.

What should the ML engineer do to confirm or deny this hypothesis?

  • A. Schedule a SageMaker Model Monitor job. Observe metrics about model quality.
  • B. Schedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
  • C. Enable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
  • D. Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.
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Suggested Answer: D 🗳️
by chris_spencer at March 12, 2025, 12:46 p.m.
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chris_spencer
1 week ago
Selected Answer: D
ModelLoadingWaitTime metric measures the time taken to load the model
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