Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team is using a generative AI model to create ad copy. They notice that the outputs sometimes include made-up statistics and false claims about their products. They want to reduce these hallucinations without retraining the model. What should they do?
⚠ Common exam trap
The trap here is assuming that fine-tuning or parameter tweaks can eliminate hallucinations without external data grounding.
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 grounding with a source of truth such as a product database.
Grounding connects the model to verified external data, ensuring responses are based on facts rather than model assumptions. It is a no-retraining approach that directly targets hallucinations by providing context. Other methods like temperature or token limits do not reliably improve factual accuracy.
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 grounding with a source of truth such as a product database.
Why this is correct
Grounding allows the model to reference an external, authoritative data source (like a product database) when generating responses. This reduces hallucinations by anchoring outputs in verified facts. It does not require retraining and can be implemented via Vertex AI's grounding features, such as using a corpus or connecting to a database.
- ✗
Decrease the maximum output tokens.
Why it's wrong here
Reducing the maximum output tokens shortens responses but does not address the root cause of hallucinations. The model may still generate false claims within the shorter output. This parameter controls length, not factual accuracy, and could truncate important information without improving truthfulness.
- ✗
Increase the model's temperature parameter.
Why it's wrong here
Increasing temperature makes the model's output more random and creative, which can actually increase hallucinations. It does not ground the model in factual data. For reducing made-up claims, lower temperature or grounding techniques are more appropriate. Temperature adjustment alone is not a reliable solution for factual accuracy.
- ✗
Fine-tune the model on a small set of correct ad copies.
Why it's wrong here
Fine-tuning can improve style and task-specific performance but may not eliminate hallucinations, especially if the training data is limited. It also requires retraining, which the team wants to avoid. Fine-tuning is not a guaranteed fix for factual inaccuracies and can even reinforce biases if data is not comprehensive.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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