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
Custom implementation is unnecessary; built-in logging is available.
- ✗
Vertex AI endpoints do not support request/response logging.
Why it's wrong here
They do support it.
- ✓
Enable request/response logging on the endpoint and create a BigQuery sink for the log.
Why this is correct
Correct: endpoint logging captures data, and a sink routes to BigQuery.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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