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1Z0-1127-25 LLM Fundamentals Practice Question

A team wants to use an LLM to answer questions about a private codebase that is updated hourly. They cannot afford to fine-tune every hour. Which OCI feature or approach is most suitable?

⚠ Common exam trap

Test-takers frequently assume fine-tuning is the only way to incorporate private or dynamic data, overlooking RAG's ability to provide real-time, cost-effective access to frequently updated information 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 Retrieval-Augmented Generation (RAG) with a vector database

Retrieval-Augmented Generation (RAG) with a vector database is the most suitable approach because it allows the LLM to answer questions about a frequently updated private codebase without retraining. RAG retrieves relevant code snippets from a vector index at query time, ensuring the model always has access to the latest code without the cost and latency of hourly fine-tuning.

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 a long-context model with full codebase in prompt
Why it's wrong here
Cannot fit entire codebase into context window.
✓
Implement Retrieval-Augmented Generation (RAG) with a vector database
Why this is correct
RAG provides up-to-date retrieval without retraining.
✗
Fine-tune a model on the codebase daily
Why it's wrong here
Daily fine-tuning is expensive and not real-time.
✗
Use a smaller model with faster inference
Why it's wrong here
Speed does not solve knowledge freshness.

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1Z0-1127-25 LLM Fundamentals Practice Question

Why each option matters

Option-by-option breakdown

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