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Deploying and Managing Generative AI on OCImediumMultiple ChoiceObjective-mapped

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

Network Topology
oci generative-ai custom-model createcompartment-id ocid1.compartment.oc1..aaaa... \dedicated-ai-cluster-id ocid1.dedicatedaicluster.oc1..bbbb... \model-name test-model \model-artifact bucket-name=my-bucketinference-configuration instance-count=1

Refer to the exhibit. An administrator receives the error shown when attempting to deploy a custom model. What is the most likely cause?

⚠ Common exam trap

Oracle often tests the distinction between 'permission denied' and 'resource not found' errors; the trap here is that candidates may confuse a missing IAM policy with an incorrect compartment ID or a corrupted artifact, but the error message's reference to 'access' or 'permission' points directly to Object Storage read rights.

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 user or service does not have permission to read the model artifact from Object Storage

The error indicates that the deployment process cannot access the model artifact stored in Object Storage. In OCI Generative AI, the service must have read permission on the bucket and object to download the artifact. If the user or service principal lacks the necessary IAM policy (e.g., `allow service generative-ai to read objects in compartment X where target.bucket.name='Y'`), the deployment fails with this access-denied error.

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 user or service does not have permission to read the model artifact from Object Storage

    Why this is correct

    The 403 error indicates lack of IAM permissions to access the bucket.

  • The compartment ID is incorrect

    Why it's wrong here

    An incorrect compartment ID would result in a different error, not 403 Forbidden.

  • The model artifact file is corrupted

    Why it's wrong here

    Corrupted files would cause a different error (e.g., 400) during artifact processing.

  • The dedicated AI cluster ID is invalid

    Why it's wrong here

    An invalid cluster ID would likely give a 404 or validation error.

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