Generative AI Leader Fundamentals of Generative AI Practice Question
A financial services firm is deploying a generative AI model to answer customer queries about investment products. They want to ensure the model's responses are based on the most current and authoritative internal documents. Which technique should they implement?
⚠ Common exam trap
The trap here is thinking that fine-tuning or a larger context window alone can keep responses current, when they lack dynamic retrieval.
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
✓
Using retrieval-augmented generation (RAG) with a vector database
Retrieval-augmented generation (RAG) dynamically retrieves relevant documents from a vector database and provides them to the model as context, ensuring responses are grounded in the most current authoritative sources. This is ideal for the financial firm's need to answer queries based on up-to-date internal documents. Other options do not provide real-time 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.
- ✓
Using retrieval-augmented generation (RAG) with a vector database
Why this is correct
RAG combines a generative model with a retrieval system that fetches relevant documents from a vector database in real time. By indexing the firm's current authoritative documents, the model can ground its responses in the latest information. This ensures accuracy and reduces hallucinations, directly meeting the requirement for responses based on current internal documents.
- ✗
Deploying the model with a larger context window
Why it's wrong here
A larger context window allows the model to consider more text at once, but it does not automatically retrieve or prioritize current authoritative documents. Without a retrieval mechanism, the model still relies on its training data, which may be outdated. The context window is a capability, not a solution for grounding in dynamic internal sources.
- ✗
Fine-tuning the model on historical customer interactions
Why it's wrong here
Fine-tuning on historical interactions can improve the model's tone and style, but it does not guarantee that responses are based on the most current authoritative documents. Historical data may become outdated quickly, and the model might still hallucinate or use stale information. The firm needs a method that dynamically incorporates up-to-date sources, which fine-tuning alone does not provide.
- ✗
Increasing the model's temperature parameter
Why it's wrong here
Temperature controls randomness in generation; higher values make outputs more diverse but less focused. This would not ground responses in authoritative documents and could increase hallucinations. The firm needs factual accuracy, not creativity, so adjusting temperature is counterproductive and does not address the need for current information.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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