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.

Show Suggested Answer

Suggested Answer: D 🗳️

by chris_spencer at March 12, 2025, 12:46 p.m.

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Comments

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snna4

1 month, 4 weeks ago

Selected Answer: C

C. ModelSetupTime because we have single model. Not D, because it relates to Multi-Model Endpoint Model Loading Metrics see https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html for ModelSetupTime in "Endpoint Invocation Metrics" and ModelLoadingWaitTime in "Multi-Model Endpoint Model Loading Metrics"

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snna4

2 months ago

Selected Answer: D

ModelSetupTime - The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container. see https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html

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snna4

1 month, 4 weeks ago

Please delete this my thread, as option D is incorrect.

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snna4

1 month, 4 weeks ago

Sorry. Option D is incorrect. The answer it C - ModelSetupTime because we have we have single model endpoint, not multi-model.

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eesa

2 months, 4 weeks ago

Selected Answer: C

C. Enable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace. ✅ Correct: This metric will show if model startup time is contributing to the latency. ✅ Directly confirms or denies the cold start hypothesis. ModelSetupTime The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container. Units: Microseconds Valid statistics: Average, Min, Max, Sample Count, Percentiles https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html

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chris_spencer

3 months, 2 weeks ago

Selected Answer: D

ModelLoadingWaitTime metric measures the time taken to load the model

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