PMLE Monitoring ML Solutions Practice Question
A company wants to log all prediction requests and responses from a Vertex AI Endpoint to BigQuery for auditing and debugging. How can they achieve this?
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
✓
Enable request/response logging on the endpoint and create a BigQuery sink for the log.
Vertex AI endpoints can be configured to enable request/response logging. The logs can be sent to a BigQuery table via a log sink.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export endpoint logs from Cloud Logging to Cloud Storage and then load into BigQuery manually.
Why it's wrong here
Exporting logs from Cloud Logging to Cloud Storage and then manually loading into BigQuery introduces a batch latency that prevents real-time auditing of prediction requests and responses, whereas the requirement implies continuous logging directly to BigQuery. This option is tempting because Cloud Logging exports to Cloud Storage are a standard method for long-term log retention and compliance archiving, and would be correct if the goal were periodic offline analysis rather than immediate, queryable audit trails.
- ✗
Use a Cloud Function to intercept predictions and write to BigQuery.
Why it's wrong here
A Cloud Function cannot intercept traffic to a Vertex AI Endpoint; predictions bypass it entirely, so requests and responses never reach the function. It is tempting as a generic event-driven glue, and it would be correct for reacting to Pub/Sub messages or HTTP triggers, not endpoint inference logging.
- ✗
Vertex AI endpoints do not support request/response logging.
Why it's wrong here
Vertex AI endpoints do support request-response logging, configurable to BigQuery or Cloud Logging, so this claim is factually false. It is tempting when documentation is unclear, but it would only be the answer if the platform genuinely lacked the feature, which it does not.
- ✓
Enable request/response logging on the endpoint and create a BigQuery sink for the log.
Why this is correct
Enabling request/response logging on the Vertex AI Endpoint captures the full prediction payloads, satisfying the audit requirement for both inputs and outputs. Routing those logs to BigQuery via a sink then makes them queryable for debugging. Sampling settings must be set to capture all traffic, since default sampling is partial.
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