1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which TWO are valid methods to monitor the performance of a generative AI model deployed on OCI Data Science?
⚠ Common exam trap
Oracle often tests the distinction between monitoring (OCI Monitoring), logging (OCI Logging), and notification/event services, so candidates mistakenly select OCI Notifications or OCI Events as monitoring tools when they are actually reactive or alerting services.
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
✓
Use OCI Monitoring service to track custom metrics like latency and throughput
OCI Monitoring service allows you to define and track custom metrics such as inference latency (e.g., p50/p99 response times) and throughput (requests per second) for your generative AI model deployed on OCI Data Science. This enables real-time performance monitoring and alerting based on thresholds you set, which is essential for production AI workloads.
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 OCI Notifications to receive alerts on model drift
Why it's wrong here
Notifications are for alerting, not monitoring; model drift requires additional tooling.
- ✓
Use OCI Monitoring service to track custom metrics like latency and throughput
Why this is correct
Allows pushing custom metrics from the inference script.
- ✓
Use OCI Logging service to collect inference logs
Why this is correct
Logs can contain timing information and errors.
- ✗
Use OCI Events service to trigger retraining on low accuracy
Why it's wrong here
Events respond to changes, but monitoring is separate.
- ✗
Use OCI Audit service to review API call logs
Why it's wrong here
Audit logs record management plane operations, not inference performance.
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