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MLS-C01 Modeling Practice Question

A data scientist trains a gradient boosting model on a large dataset using SageMaker. The training completes successfully, but when deploying the model to a real-time endpoint, inference latency is too high. Which change is MOST likely to reduce latency without significant accuracy loss?

⚠ Common exam trap

It's easy for candidates to confuse scaling the endpoint (Option A) as the primary fix for latency, when the real issue is model complexity that can be reduced through pruning without significant accuracy loss.

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

Prune the trees by removing nodes with low importance

Pruning trees by removing nodes with low importance reduces the model's complexity, which directly decreases inference latency because fewer decision paths need to be evaluated. In gradient boosting, this can be done with minimal accuracy loss if the removed nodes correspond to splits that contribute little to the overall prediction, as measured by feature importance or gain.

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 not address model complexity.

  • Prune the trees by removing nodes with low importance

    Why this is correct

    Pruning reduces model size and inference time.

  • Increase the number of trees in the ensemble

    Why it's wrong here

    More trees increase latency.

  • Use SageMaker Batch Transform instead of real-time

    Why it's wrong here

    Batch Transform is for offline predictions, not reducing latency for real-time.

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Written by Johnson Ajibi, MSc IT Security

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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.