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OCI Generative AI ServicehardMultiple SelectObjective-mapped

1Z0-1127-25 OCI Generative AI Service Practice Question

A company wants to deploy a fine-tuned model for real-time inference with consistent low latency. They are evaluating dedicated AI clusters. Which THREE factors should they consider when provisioning the cluster?

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

Number of clusters (for high availability)

Model units determine compute capacity and cost, cluster size affects availability and fault tolerance, and the region impacts data residency and latency. The base model is already chosen, and fine-tuning timeout is irrelevant for inference.

Answer analysis

Option-by-option breakdown

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

  • Fine-tuning job timeout settings

    Why it's wrong here

    Timeout settings are for training, not for inference cluster provisioning.

  • Number of clusters (for high availability)

    Why this is correct

    Multiple clusters provide redundancy and fault tolerance.

  • Region where the cluster is provisioned

    Why this is correct

    Region affects latency to end users and data residency requirements.

  • Number of model units per cluster

    Why this is correct

    Model units define the compute capacity dedicated to inference.

  • The base model used for fine-tuning

    Why it's wrong here

    The base model is already fixed at fine-tuning time; it does not affect cluster provisioning.

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