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Monitoring ML SolutionshardMultiple SelectObjective-mapped

PMLE Monitoring ML Solutions Practice Question

An ML engineer is troubleshooting why a Vertex AI Endpoint is returning high prediction latency. They have enabled request/response logging and see that some requests take >1 second while most are fast. Which THREE actions should they take 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

Check CPU and GPU utilisation metrics on the endpoint.

Check Cloud Monitoring metrics for p99 latency trends, CPU/GPU utilisation to identify bottlenecks, and review request/response logs for patterns (e.g., large payloads causing slow inference).

Answer analysis

Option-by-option breakdown

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

  • Check CPU and GPU utilisation metrics on the endpoint.

    Why this is correct

    High utilisation can indicate resource bottleneck.

  • Review the request/response logs in BigQuery to identify if large payloads correlate with high latency.

    Why this is correct

    Logs can show payload size and latency per request.

  • Disable Vertex AI Model Monitoring to reduce overhead.

    Why it's wrong here

    Monitoring overhead is minimal and not the cause of high latency.

  • Check the p99 latency metric in Cloud Monitoring.

    Why this is correct

    Essential to confirm the latency issue.

  • Increase the number of replicas to resolve the issue immediately.

    Why it's wrong here

    Scaling without diagnosis may not address root cause; should diagnose first.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.