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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A company wants to create a chatbot that answers questions based on a large internal document set that is updated weekly. They have limited ML expertise. Which approach is recommended?

⚠ Common exam trap

The exam often tests the misconception that fine-tuning is the only way to incorporate custom data, when in fact RAG is the preferred method for dynamic, large-scale document sets due to its cost-effectiveness, ease of updates, and lower ML expertise requirements.

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 a vector database.

Retrieval-Augmented Generation (RAG) with a vector database is the recommended approach because it allows the chatbot to answer questions based on a large, frequently updated document set without requiring model retraining. RAG retrieves relevant document chunks at query time using vector similarity search, then passes them as context to the LLM, ensuring up-to-date answers with minimal ML expertise.

Answer analysis

Option-by-option breakdown

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

✗
Fine-tune a model on the entire document set.
Why it's wrong here
Incorrect: Requires frequent retraining with weekly updates.
✗
Train a custom model from scratch.
Why it's wrong here
Incorrect: Too resource-intensive and complex.
✗
Include all documents in the system prompt.
Why it's wrong here
Incorrect: Exceeds context limits.
✓
Use retrieval-augmented generation (RAG) with a vector database.
Why this is correct
Correct: RAG handles dynamic data without retraining.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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