1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A DevOps engineer is setting up monitoring and logging for a generative AI inference endpoint. Which three resources should they enable? (Select THREE.)
⚠ Common exam trap
Many exam-takers confuse OCI Audit logs (which track administrative API calls) with OCI Logging (which captures data-plane request/response details), leading them to select Audit logs instead of Logging for monitoring inference payloads.
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
✓
OCI Logging for inference requests and responses
OCI Logging captures detailed logs of inference requests and responses, which is essential for auditing, debugging, and analyzing the behavior of a generative AI endpoint. This service provides a centralized repository for log data, enabling DevOps engineers to track input prompts and model outputs for compliance and troubleshooting purposes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI VCN flow logs for network traffic
Why it's wrong here
Incorrect: Flow logs are for network security analysis, not application monitoring.
- ✓
OCI Logging for inference requests and responses
Why this is correct
Correct: Logging allows auditing and debugging of inference calls.
- ✓
OCI Monitoring metrics for endpoint latency and error rates
Why this is correct
Correct: Metrics drive performance dashboards and alerts.
- ✓
OCI Application Performance Monitoring (APM) for tracing inference requests
Why this is correct
Correct: APM provides distributed tracing to diagnose performance bottlenecks.
- ✗
OCI Audit logs for all API calls
Why it's wrong here
Incorrect: Audit logs capture control plane operations, not inference data.
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