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Machine Learning Implementation and OperationsmediumMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

A deployed SageMaker endpoint is returning high latency. The model is a scikit-learn Random Forest. Which action is most likely to reduce latency?

⚠ Common exam trap

Many candidates confuse latency (per-request time) with throughput (requests per second) and incorrectly choose scaling out instances (Option C), or assume GPU acceleration universally speeds up inference (Option D), ignoring that scikit-learn models are CPU-only.

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

Reduce the number of trees in the ensemble

Reducing the number of trees in a Random Forest ensemble directly decreases the total number of decision paths that must be evaluated per inference request. Since each tree contributes additively to the prediction time, fewer trees means fewer sequential or parallel evaluations, which lowers the per-request latency at the cost of some model accuracy.

Answer analysis

Option-by-option breakdown

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

  • Reduce the number of trees in the ensemble

    Why this is correct

    Fewer trees reduce computation time per inference.

  • Prune decision trees in the model

    Why it's wrong here

    Pruning can reduce size but may hurt accuracy; not standard practice.

  • Increase the number of instances behind the endpoint

    Why it's wrong here

    More instances handle more requests but may not reduce latency per request.

  • Switch to a GPU instance type

    Why it's wrong here

    GPU acceleration is not beneficial for Random Forest inference.

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