easyMultiple Choice
PMLE Practice Question: A machine learning model deployed on Vertex AI is…
A machine learning model deployed on Vertex AI is returning erroneous predictions. The team needs to investigate the root cause by examining the prediction request and response details. Which Google Cloud tool is best suited for this?
⚠ Common exam trap
Candidates often confuse Cloud Monitoring (which shows aggregate health metrics) with Cloud Logging (which provides granular request/response data), leading them to choose a tool that cannot reveal the specific prediction details needed for root cause analysis.
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
✓
Cloud Logging
Cloud Logging is the correct tool because it captures detailed logs of prediction requests and responses, including input features, model outputs, and any errors. By examining these logs, the team can trace the exact data flow and identify discrepancies causing erroneous predictions, such as data preprocessing issues or model version mismatches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Monitoring
Why it's wrong here
Cloud Monitoring collects metrics, dashboards and alerting on resource health; it does not retain individual prediction request and response bodies. It is tempting because it is the default observability surface for Vertex AI endpoints, and would be correct for tracking latency, error rates or resource saturation rather than inspecting payload contents.
- ✗
Cloud Debugger
Why it's wrong here
Cloud Debugger inspects live application state via snapshots and logpoints in running code, not the request/response payloads Vertex AI records. It is tempting because it debugs production code without redeploying, which suits diagnosing application logic faults in App Engine or GKE services, but it cannot surface prediction traffic details.
- ✓
Cloud Logging
Why this is correct
Cloud Logging captures the raw prediction request and response payloads emitted by Vertex AI endpoints, letting the team inspect exact inputs and outputs to trace erroneous predictions. It satisfies the need to examine request and response details, which aggregate metrics alone cannot expose.
- ✗
Cloud Trace
Why it's wrong here
Cloud Trace records latency and span timing across distributed services, capturing performance data rather than the request and response payloads needed to diagnose wrong predictions. It would be the right tool for profiling slow inference latency, not for inspecting prediction inputs and outputs.
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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.