PMLE Monitoring ML Solutions Practice Question
A data scientist notices that the model's prediction latency has increased over the last week. They need to investigate the root cause by examining request and response logs for the Vertex AI Endpoint. What is the recommended way to capture these logs?
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
✓
Configure request/response logging by specifying a BigQuery destination in the endpoint deployment
Vertex AI Endpoint can be configured to log request/response data to BigQuery via a log sink. This data can then be analyzed to understand latency issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Vertex AI Model Monitoring with sampling rate 100%
Why it's wrong here
Model Monitoring does not capture full request/response payloads; it captures feature values for skew/drift analysis.
- ✗
Export logs to Cloud Storage using a sink and use gcloud logging read
Why it's wrong here
This captures only log text, not the full request/response payloads needed for latency analysis.
- ✗
Use Cloud Monitoring custom metrics to capture latency per request
Why it's wrong here
Custom metrics can monitor latency but not capture request/response payloads for root cause analysis.
- ✓
Configure request/response logging by specifying a BigQuery destination in the endpoint deployment
Why this is correct
Correct: Vertex AI Endpoints can log request/response to BigQuery when enabled in the deployment.
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