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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
Option B is correct because monitoring prediction accuracy in production via request/response logging and Cloud Monitoring alerts is essential to detect model drift, data skew, and degradation, allowing timely retraining or rollback to maintain reliability. Option E is correct because gradually shifting traffic to new model versions using canary deployment on a Vertex AI Endpoint lets you validate the new model against live traffic and roll back quickly if metrics degrade, minimizing risk. Option A is not appropriate because batch predictions cannot serve real-time production traffic and do not by themselves ensure reliability. Option C is wrong because disabling request/response logging removes the observability needed to detect accuracy issues and violates reliability monitoring. Option D is wrong because routing all traffic to a single model endpoint without versioning or traffic splitting prevents safe canary testing and rollback.
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
Batch predictions cannot serve synchronous online requests, so the endpoint's real-time reliability requirement goes unmet entirely. Batch is tempting because it avoids live-serving failures. It is correct for offline scoring, scheduled bulk inference, or latency-tolerant pipelines, not production online endpoints.
- ✓
Monitor prediction accuracy in production with logging and alerts
Why this is correct
Logging requests and responses to BigQuery or Cloud Logging, then alerting on drift or accuracy degradation, detects reliability failures in live traffic. This satisfies the production reliability requirement by surfacing silent model decay before it affects business outcomes, enabling timely retraining or rollback.
- ✗
Disable request/response logging to reduce latency
Why it's wrong here
Disabling request/response logging removes the telemetry needed to detect drift, errors, and degradation, undermining reliability monitoring rather than improving it. Logging is tempting to cut latency. It is appropriate only where privacy rules forbid payload capture, and even then metadata logging should remain.
- ✗
Use a single model endpoint for all traffic
Why it's wrong here
A single endpoint concentrates all traffic on one deployed model, so a failure or resource exhaustion removes every replica simultaneously. Multiple endpoints with traffic splitting isolate faults. Single endpoints suit low-risk dev or test workloads where redundancy costs outweigh availability needs.
- ✓
Gradually shift traffic to new model versions (canary deployment)
Why this is correct
Canary deployment routes a small traffic percentage to the new version while the stable version serves the remainder, so regressions surface on limited requests and rollback is immediate. This satisfies the reliability requirement by limiting blast radius during version promotion.
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