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Machine Learning Implementation and OperationsmediumMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

A company is using Amazon SageMaker to host a model for real-time inference. The model was trained using SageMaker's built-in Linear Learner algorithm. The endpoint has been running for a week, and the operations team notices that the endpoint's latency has increased from 50 ms to 150 ms over the past few days. The number of requests per second has remained steady at about 200. The team suspects a memory leak in the inference container. What should the team do to diagnose the issue?

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 CloudWatch Logs and use Container Insights to view memory utilization.

CloudWatch Container Insights provides metrics for containerized applications, including memory utilization. This can help diagnose a memory leak by showing memory usage trends over time. Option B is incorrect because CloudWatch latency metrics only show endpoint response time, not memory usage. Option C is incorrect because SageMaker Debugger is designed for debugging training jobs, not inference containers. Option D is incorrect because SageMaker Model Monitor detects data and model drift, not memory leaks.

Answer analysis

Option-by-option breakdown

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

  • Enable CloudWatch Logs and use Container Insights to view memory utilization.

    Why this is correct

    Container Insights shows memory usage trends, helping diagnose leaks.

  • Use Amazon CloudWatch to monitor the endpoint's latency metric.

    Why it's wrong here

    Latency metric shows the symptom but not the cause (memory).

  • Use SageMaker Debugger to inspect the inference container.

    Why it's wrong here

    Debugger is for training, not inference.

  • Use SageMaker Model Monitor to detect data drift.

    Why it's wrong here

    Model Monitor detects input/output drift, not system issues.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.