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Machine Learning Implementation and OperationshardMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

Exhibit

2023-01-01 12:00:00,000 - ERROR - Model prediction took 15 ms for request ID abc123

Refer to the exhibit. A data scientist is reviewing CloudWatch logs for a SageMaker real-time endpoint. The log shows that a prediction took 15 ms. The endpoint is configured with an ml.c5.large instance and the model is a small scikit-learn model. The latency requirement is under 10 ms. Which action would most likely reduce the latency?

⚠ Common exam trap

Test-takers frequently confuse horizontal scaling (adding instances) with reducing latency, but horizontal scaling only improves throughput, not the per-request response time, which is the key metric in this question.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Use a larger instance type

The latency of 15 ms exceeds the 10 ms requirement, indicating that the current ml.c5.large instance lacks sufficient compute resources (CPU) to process predictions quickly enough. Upgrading to a larger instance type (e.g., ml.c5.xlarge or ml.c5.2xlarge) provides more CPU capacity, reducing inference time by allowing the model to compute predictions faster. This directly addresses the bottleneck for a small scikit-learn model, which is CPU-bound and benefits from increased compute power.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use a larger instance type

    Why this is correct

    More CPU power reduces latency.

  • Add more instances to the endpoint

    Why it's wrong here

    Adding instances increases throughput, not reduces latency.

  • Change the model to a TensorFlow model

    Why it's wrong here

    Framework change may not reduce latency.

  • Enable SageMaker Batch Transform

    Why it's wrong here

    Batch transform is not real-time.

  • Increase the batch size for inference

    Why it's wrong here

    Batch size is for batch transform, not real-time.

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