easyMultiple Choice
PDE Practice Question: A team is deploying a model on AI Platform…
A team is deploying a model on AI Platform Prediction. They want to monitor for data drift to maintain model quality. Which service should they use?
⚠ Common exam trap
Google often tests the misconception that general-purpose monitoring or logging services (like Cloud Monitoring or Audit Logs) are sufficient for ML-specific drift detection, when in fact only a dedicated ML evaluation service like AI Platform Continuous Evaluation provides the necessary statistical comparison against training 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
✓
AI Platform Continuous Evaluation
AI Platform Continuous Evaluation (CE) is the correct service because it is specifically designed to monitor deployed models for data drift and feature skew. It automatically compares the distribution of incoming prediction requests against the training data distribution, alerting when statistically significant drift is detected, which directly addresses the need to maintain model quality over time.
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 DLP
Why it's wrong here
Cloud DLP discovers and redacts sensitive data such as personally identifiable information, but it does not compare live feature distributions against training baselines. It is the correct choice for classifying and de-identifying data before it reaches storage.
- ✓
AI Platform Continuous Evaluation
Why this is correct
AI Platform Continuous Evaluation monitors deployed models for data drift and skew by comparing serving inputs against training baselines, directly satisfying the drift-monitoring requirement. It reports divergence metrics so the team can retrain before model quality degrades.
- ✗
Cloud Monitoring
Why it's wrong here
Cloud Monitoring tracks infrastructure metrics such as CPU and latency, not feature distributions, so it cannot detect drift in input data. It is tempting because it is the native alerting service for AI Platform Prediction, and it would be correct for operational monitoring of resource health or prediction error rates.
- ✗
Cloud Audit Logs
Why it's wrong here
Cloud Audit Logs records administrative and data-access activity, not the statistical distribution of prediction inputs, so it cannot detect drift. It is tempting because it does capture prediction request metadata, and it would be the right choice for auditing who called the model or investigating suspicious access, rather than monitoring feature distributions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.