easyMultiple Choice
PMLE Practice Question: A data scientist wants to log prediction inputs…
A data scientist wants to log prediction inputs and outputs for model monitoring. Which Google Cloud service is best suited for this?
⚠ Common exam trap
Google Cloud often tests the distinction between logging (Cloud Logging) and monitoring (Cloud Monitoring), where candidates mistakenly choose Cloud Monitoring because they think 'monitoring' includes logging, but Cloud Monitoring is for metrics and alerts, not for storing and querying log data.
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 best choice because it is designed to ingest, store, and analyze log data, including custom log entries from applications. The data scientist can use the Cloud Logging API to write structured log entries containing prediction inputs and outputs, then query them using Logs Explorer or export them for further analysis. This aligns with the requirement to log prediction inputs and outputs for model monitoring, as Cloud Logging provides a centralized, scalable, and queryable log management service.
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 ingests time-series metrics and alerting policies, not per-request feature vectors and predictions; its API accepts metric descriptors, not arbitrary payloads. It is tempting because it is the observability service, and it would be correct for latency, error-rate and resource-utilisation dashboards rather than input/output logging.
- ✗
Cloud Storage
Why it's wrong here
Cloud Storage holds objects but provides no queryable, row-level store for prediction records; retrieving individual inputs and outputs for drift analysis means scanning objects. It is tempting as cheap durable storage, and it would be correct for archiving exported batch prediction files or model artefacts, not for serving-time logging.
- ✓
Cloud Logging
Why this is correct
Cloud Logging captures prediction inputs and outputs as log entries, providing the raw request and response records that Vertex AI Model Monitoring and downstream analysis consume. It is the native service for storing and querying these prediction payloads.
- ✗
BigQuery
Why it's wrong here
BigQuery is a warehouse for batch analytics, not the managed endpoint-logging sink; it lacks the automatic capture of online prediction requests and responses. It is tempting because logged data is later analysed with SQL, and it would be correct for storing exported logs or training data, not for direct prediction logging.
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JA
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.