hardMultiple Choice
PDE Practice Question: A company has a production machine learning model…
A company has a production machine learning model deployed on Vertex AI Endpoint that predicts customer churn. The model is retrained weekly using a Vertex AI Pipeline that pulls new data from BigQuery. Recently, the model's accuracy has been declining. The data science team suspects data drift but is unsure. They have enabled Vertex AI Model Monitoring but have not set up any alerts. The team wants to diagnose and address the issue quickly. The pipeline runs successfully, and no errors are reported. The model endpoint is serving predictions with average latency of 200ms. What should the team do first?
⚠ Common exam trap
Google Cloud often tests the misconception that any model performance decline must be fixed by immediate retraining or infrastructure scaling, when the correct first step is always to diagnose the root cause using the monitoring tools already in place.
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
✓
Review Vertex AI Model Monitoring drift reports and set up alerts for significant drift
The team has already enabled Vertex AI Model Monitoring, which automatically tracks feature distributions and prediction statistics over time. The first diagnostic step should be to review the drift reports generated by Model Monitoring to confirm whether data drift is occurring, and then set up alerts so the team is proactively notified of significant drift in the future. This directly addresses the suspected root cause without unnecessary operational changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Immediately trigger a retraining pipeline with more recent data
Why it's wrong here
Retraining before confirming drift risks baking the same skewed distribution into the model and wastes a pipeline run. Immediate retraining is correct once monitoring statistics or alerts have confirmed drift, which has not yet been established.
- ✗
Increase the number of replicas to reduce latency
Why it's wrong here
Latency of 200ms is unrelated to declining accuracy, so adding replicas addresses throughput, not drift. Replica scaling is correct when the endpoint is saturated and prediction latency breaches its SLO, which the stem explicitly rules out.
- ✗
Examine Cloud Logging for prediction errors
Why it's wrong here
Prediction requests are succeeding and the pipeline reports no errors, so Cloud Logging holds no drift signal. Log inspection is correct when the endpoint returns errors, timeouts or malformed responses, none of which are occurring here.
- ✓
Review Vertex AI Model Monitoring drift reports and set up alerts for significant drift
Why this is correct
Model Monitoring already computes drift metrics, so reviewing its drift reports confirms whether feature or prediction drift explains the accuracy decline. Enabling alerts on significant drift then provides ongoing notification, satisfying the need to diagnose and address the issue quickly without pipeline changes.
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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.