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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

A company notices that some inference requests to their deployed model on OCI Generative AI take longer than acceptable. They want to reduce per-request latency. What should they do?

⚠ Common exam trap

Oracle often tests the distinction between latency (per-request speed) and throughput (requests per second), causing candidates to confuse batching or scaling replicas (which improve throughput) with reducing individual request latency.

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

Reduce the maximum number of tokens generated

Reducing the maximum number of tokens generated directly decreases the amount of computation required per inference request because the model stops generating output earlier. Since latency is proportional to the number of output tokens produced, this is the most effective single change to reduce per-request response time in OCI Generative AI deployments.

Answer analysis

Option-by-option breakdown

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

  • Reduce the maximum number of tokens generated

    Why this is correct

    Lowering max tokens reduces the amount of computation per request, directly decreasing latency.

  • Enable request batching

    Why it's wrong here

    Batching can improve throughput but may increase per-request latency due to wait time.

  • Use a larger model to improve accuracy

    Why it's wrong here

    Larger models increase computation and latency.

  • Increase the number of replicas in the deployment

    Why it's wrong here

    Increasing replicas improves concurrency but does not reduce latency for individual requests.

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