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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

An AI team is fine-tuning a large language model using OCI Data Science and plans to deploy the fine-tuned model using the Generative AI service's custom model deployment. What is the required format for the model artifacts?

⚠ Common exam trap

Test-takers frequently confuse OCI Generative AI's custom model deployment with OCI Data Science model deployment, which does support Docker images, leading them to incorrectly select Option C.

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

A .zip archive containing model weights and configuration files

The OCI Generative AI service requires custom model artifacts to be packaged as a .zip archive containing the model weights, configuration files (e.g., config.json, tokenizer files), and any necessary inference code. This format ensures the service can extract and load the model correctly into its managed inference infrastructure, aligning with the standard Hugging Face model repository structure.

Answer analysis

Option-by-option breakdown

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

  • A Git repository URL

    Why it's wrong here

    Git URLs are not supported; artifacts must be uploaded to Object Storage.

  • A single .pth file

    Why it's wrong here

    A single file is insufficient; the model artifacts must include configuration and vocabulary files.

  • A Docker image with the model and inference code

    Why it's wrong here

    Docker images are used for OCI Data Science deployments, not for Generative AI custom model.

  • A .zip archive containing model weights and configuration files

    Why this is correct

    The custom model deployment requires a zip archive with all necessary files.

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