1Z0-1127-25 Prompt Engineering 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?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuning is the only way to incorporate new knowledge into an LLM, but the trap here is that candidates overlook RAG's ability to handle dynamic, frequently updated documents without retraining, making it the most efficient and scalable solution.
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
Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store at inference time, without requiring model retraining. This decouples the knowledge base from the model weights, enabling monthly document updates by simply re-indexing the vector store, which is far more cost-effective and faster than fine-tuning or retraining.
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 base LLM on the policy documents monthly
Why it's wrong here
Fine-tuning is expensive and time-consuming; monthly cycles are impractical.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
RAG retrieves relevant chunks at query time, ensuring current answers without model retraining.
- ✗
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 is expensive, may hit context limits, and does not scale.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Training from scratch requires massive compute and time, not suitable for monthly updates.
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