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Machine Learning Implementation and OperationsmediumMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

A company has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?

⚠ Common exam trap

AWS often tests the misconception that improving per-request performance (e.g., via larger instances, Elastic Inference, or Neo compilation) is the solution for handling traffic spikes, when the actual need is horizontal scaling to increase request throughput capacity.

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

Enable SageMaker endpoint auto-scaling

Enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.

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 for the endpoint

    Why it's wrong here

    Larger instance may reduce latency but is not the best for spikes; auto-scaling is more cost-effective.

  • Attach SageMaker Elastic Inference to the endpoint

    Why it's wrong here

    Elastic Inference reduces per-request latency but does not handle traffic spikes.

  • Enable SageMaker endpoint auto-scaling

    Why this is correct

    Auto-scaling adds instances during spikes, reducing latency.

  • Use SageMaker Neo to compile the model

    Why it's wrong here

    Neo optimizes for edge devices, not for server-side inference.

  • Switch to SageMaker batch transform

    Why it's wrong here

    Batch transform is for offline inference, not real-time.

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