Quick Answer
The answer is OCI Monitoring, the native telemetry service that collects and stores metrics like token usage and latency from OCI Generative AI endpoints. This is correct because OCI Generative AI automatically emits metrics such as input/output token counts and model inference latency to the Monitoring service, which you can query via the API or visualize in the Console for cost optimization. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of which OCI service handles observability for AI workloads, often appearing as a straightforward service-mapping question. A common trap is confusing OCI Monitoring with Logging or Events, but remember: Monitoring captures numeric metrics (tokens, latency), while Logging captures text logs. Memory tip: think “Metrics = Monitoring” — if you can count it (tokens, milliseconds), Monitoring tracks it.
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
OCI Monitoring
OCI Monitoring collects and visualizes metrics such as token count and latency.
OCI Events
Why wrong: Events trigger actions based on changes, not for continuous metric collection.
OCI Notifications
Why wrong: Notifications are for alerting, not metric collection.
OCI Logging
Why wrong: Logging captures logs, not metrics. Token usage and latency are metric data.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that candidates 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.
Detailed technical explanation
OCI Monitoring uses the Metrics API to ingest data points with dimensions (e.g., model_id, endpoint_id) and supports aggregation over configurable intervals (e.g., 1-minute resolution). The Generative AI service emits metrics like `ai_model_inference_latency_milliseconds` and `ai_model_token_count` under the `oci_ai_generative_ai` namespace, which can be used to set up alarms for cost thresholds or performance degradation. Under the hood, metrics are stored in a time-series database and can be exported to OCI Streaming for further analysis.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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FAQ
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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