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LLM FundamentalsmediumMultiple ChoiceObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

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 Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store

RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer analysis

Option-by-option breakdown

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

  • Train a custom model from scratch on the policy documents each month

    Why it's wrong here

    Training from scratch requires massive compute resources and weeks of time — entirely disproportionate for monthly document updates.

  • Use a larger foundation model with a longer context window and paste all documents into each prompt

    Why it's wrong here

    Pasting all documents into every prompt is expensive, hits context limits for large document sets, and does not scale as the document library grows.

  • Fine-tune a base LLM on the policy documents monthly

    Why it's wrong here

    Fine-tuning is expensive and time-consuming; monthly cycles are impractical and fine-tuned knowledge becomes stale immediately after cutoff.

  • Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store

    Why this is correct

    RAG retrieves relevant document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.

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