1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which two metrics would you monitor to ensure a generative AI deployment on OCI is operating efficiently? (Choose two.)
⚠ Common exam trap
Oracle often tests the distinction between model quality metrics (like accuracy) and operational efficiency metrics (like latency and throughput), so the trap here is that candidates mistakenly select 'Model accuracy on validation set' because they confuse model performance with deployment performance.
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
✓
Request throughput (requests per second)
Request throughput (requests per second) is a critical metric for monitoring the operational efficiency of a generative AI deployment on OCI because it directly measures the system's capacity to handle incoming inference requests. If throughput drops below expected levels, it indicates a bottleneck in the compute resources (e.g., GPU utilization) or the serving infrastructure, which can lead to degraded user experience and potential timeouts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Number of active users
Why it's wrong here
Active users measure usage, not efficiency of the deployment.
- ✗
Object Storage bucket size
Why it's wrong here
Bucket size is unrelated to inference performance.
- ✓
Request throughput (requests per second)
Why this is correct
Throughput indicates how many requests the system can handle.
- ✓
Average inference latency
Why this is correct
Latency indicates responsiveness and user experience.
- ✗
Model accuracy on validation set
Why it's wrong here
Accuracy is a model quality metric, not deployment efficiency.
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