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Deploying and Managing Generative AI on OCIhardMultiple ChoiceObjective-mapped

1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question

Exhibit

GET /20180401/metrics?compartmentId=ocid1.compartment.oc1..aaaa...&metricName=InferenceLatency&aggregationInterval=1m&groupBy=modelId

Refer to the exhibit. The dashboard shows latency grouped by modelId, but some points are missing for certain modelIds. Which of the following is the most likely reason?

⚠ Common exam trap

Watch out — candidates often confuse missing data due to inactive resources with configuration errors (e.g., metric name typos or compartment mismatches), but OCI exam tests the understanding that metric gaps are often caused by resource lifecycle events rather than misconfiguration.

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

The modelIds with missing data may have been deleted or are inactive

In OCI's Generative AI service, model deployments are associated with specific modelIds. If a modelId is deleted or its deployment is deactivated, the corresponding telemetry data (e.g., latency metrics) will no longer be reported, causing gaps in the dashboard. The dashboard aggregates metrics only for active modelIds, so missing points indicate that those modelIds are no longer in service.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The metric name is misspelled

    Why it's wrong here

    A misspelled metric name would return no data for any model.

  • The aggregation interval is too short

    Why it's wrong here

    A short interval would still produce data points for all models; it would not cause missing points for specific models.

  • The modelIds with missing data may have been deleted or are inactive

    Why this is correct

    Inactive or deleted models stop emitting metrics, leading to gaps in the time series.

  • The compartmentId is incorrect

    Why it's wrong here

    An incorrect compartmentId would return data for a different compartment, potentially missing all models, not just some.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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