1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company has deployed a generative AI model endpoint on OCI. They want to monitor token usage and latency for cost optimization. Which OCI service should they use to collect these metrics?
⚠ Common exam trap
Many exam-takers confuse OCI Logging (which collects unstructured logs) with OCI Monitoring (which collects structured metrics), leading them to select Logging for numeric performance data like token counts and latency.
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 Monitoring
A is correct because OCI Monitoring is the native telemetry service that collects and stores metrics such as token usage (e.g., input/output token counts) and latency (e.g., model inference latency) from OCI Generative AI endpoints. These metrics are automatically emitted by the OCI Generative AI service and can be queried via the Monitoring API or visualized in the Console, enabling cost optimization by tracking consumption patterns.
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 Monitoring
Why this is correct
OCI Monitoring collects and visualizes metrics such as token count and latency.
- ✗
OCI Events
Why it's wrong here
Events trigger actions based on changes, not for continuous metric collection.
- ✗
OCI Notifications
Why it's wrong here
Notifications are for alerting, not metric collection.
- ✗
OCI Logging
Why it's wrong here
Logging captures logs, not metrics. Token usage and latency are metric data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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