Generative AI Leader Fundamentals of Generative AI Practice Question
A support team is using a generative AI model to answer customer questions from an internal knowledge base. They notice that when the same question is asked twice, the model sometimes gives different answers, and occasionally includes details not found in the knowledge base. They want to reduce variability and keep responses closer to the source content. Which action should they take?
⚠ Common exam trap
The trap here is thinking a bigger model or longer output solves hallucination, when the real levers are sampling temperature and providing grounded source content.
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
✓
Decrease the temperature parameter and ground responses with retrieved knowledge base content.
Reducing temperature makes sampling more deterministic, so identical prompts tend to yield consistent answers. Grounding the model with retrieved knowledge base passages supplies authoritative context and reduces hallucinated details. Used together, these changes directly target both observed issues: inconsistent responses and content not present in the source material.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter to encourage more creative responses.
Why it's wrong here
Increasing temperature makes the model's output more random and diverse, which would worsen the variability the team is trying to fix. Higher temperature also raises the chance of unsupported details. The team needs more deterministic and grounded behavior, so raising temperature moves in the opposite direction of their goal.
- ✓
Decrease the temperature parameter and ground responses with retrieved knowledge base content.
Why this is correct
Lowering temperature reduces randomness so repeated prompts produce more consistent answers, while grounding with retrieved internal content constrains the model to the provided facts. Together they address both symptoms: variable wording and invented details. This is the standard approach for support assistants that must stay faithful to an approved knowledge base.
- ✗
Switch to a larger model without changing the prompt or parameters.
Why it's wrong here
A larger model may improve general quality, but it will not automatically eliminate hallucinated details or make responses deterministic. Without grounding and lower temperature, the same variability and unsupported content can persist. Model size alone does not guarantee faithfulness to a specific internal knowledge base.
- ✗
Increase the maximum output tokens so the model has more room to explain its reasoning.
Why it's wrong here
Maximum output tokens controls response length, not factual accuracy or consistency. Allowing longer answers can actually give the model more opportunity to add unsupported statements. It does not reduce run-to-run variability, so it fails to solve the two problems the support team reported.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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