mediumMultiple SelectObjective-mapped
PDE Practice Question: Which TWO actions should you take to ensure model…
Which TWO actions should you take to ensure model reliability in a production Vertex AI Endpoint?
⚠ Common exam trap
Google Cloud often tests the misconception that disabling logging improves reliability by reducing latency, when in fact it removes the observability needed to detect and diagnose failures, which is a core tenet of MLOps reliability.
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
✓
Monitor prediction accuracy in production with logging and alerts
Monitoring prediction accuracy with logging and alerts (B) is essential for detecting model drift, data drift, and performance degradation in production. Vertex AI provides model monitoring features that automatically log prediction requests and responses, compute statistics, and trigger alerts when skew or drift thresholds are breached, enabling proactive remediation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use only batch predictions to avoid real-time issues
Why it's wrong here
Some use cases require real-time.
- ✓
Monitor prediction accuracy in production with logging and alerts
Why this is correct
Detects model degradation.
- ✗
Disable request/response logging to reduce latency
Why it's wrong here
Logging is crucial for monitoring.
- ✗
Use a single model endpoint for all traffic
Why it's wrong here
No gradual rollout, high risk.
- ✓
Gradually shift traffic to new model versions (canary deployment)
Why this is correct
Allows safe rollout and rollback.
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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.