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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

A company is using OCI Generative AI service to power a customer support chatbot. They observe that the chatbot sometimes provides outdated information because the model was trained on data up to 2022. They want to incorporate real-time knowledge without retraining the model. Which approach should they use?

⚠ Common exam trap

Candidates often confuse prompt engineering (Option B) as a way to 'override' training data, but in reality, prompt instructions cannot erase the model's learned parameters, making RAG the only viable solution for real-time knowledge without retraining.

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

Implement a Retrieval-Augmented Generation (RAG) pattern using OCI OpenSearch.

Retrieval-Augmented Generation (RAG) allows the model to access real-time information from an external knowledge base, such as OCI OpenSearch, without retraining. This pattern retrieves relevant documents or data at inference time and injects them into the prompt, enabling the model to answer with up-to-date context. It directly addresses the need for real-time knowledge while keeping the base model static.

Answer analysis

Option-by-option breakdown

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

  • Increase the max-tokens parameter to allow longer responses.

    Why it's wrong here

    Max tokens controls output length, not knowledge currency.

  • Use prompt engineering to instruct the model to ignore old information.

    Why it's wrong here

    Prompt engineering cannot reliably replace outdated knowledge with new facts.

  • Implement a Retrieval-Augmented Generation (RAG) pattern using OCI OpenSearch.

    Why this is correct

    RAG retrieves relevant up-to-date documents and feeds them to the model, enabling current responses without retraining.

  • Fine-tune the model with recent data from 2023 onwards.

    Why it's wrong here

    Fine-tuning requires retraining and is not suitable for real-time updates.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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