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

A company wants to process a large batch of documents to generate summaries using OCI Generative AI. They need the most cost-effective option without compromising on summary quality. Which approach should they use?

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

Use the on-demand (shared) inference endpoint with the Summarisation API

On-demand pricing is pay-per-token, suitable for batch workloads without dedicated capacity; it's cost-effective for sporadic or large batch jobs.

Answer analysis

Option-by-option breakdown

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

  • Use the on-demand (shared) inference endpoint with the Summarisation API

    Why this is correct

    On-demand tokens are cost-effective for batch summarization.

  • Use OCI Generative AI Agents with a knowledge base

    Why it's wrong here

    Agents are for RAG, not for batch summarization.

  • Provision a dedicated AI cluster for the batch job

    Why it's wrong here

    Dedicated clusters are for low-latency inference and are more expensive for batch workloads.

  • Fine-tune a model on summarization and deploy it on a dedicated cluster

    Why it's wrong here

    Overkill for batch summarization; fine-tuning and dedicated deployment add cost.

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