CCAO-F Claude Model Fundamentals Practice Question
When designing a prompt for Claude, which technique is most effective at reducing the risk of 'hallucination' or factually incorrect information?
⚠ Common exam trap
Candidates often select 'giving the model more creative freedom' or 'increasing the temperature', which directly contradicts the goal of factual accuracy and hallucination reduction.
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
✓
Instructing the model to admit when it lacks information.
Providing clear, constrained instructions and grounding the model in provided context is the most effective way to minimize hallucinations. By explicitly instructing the model to reply 'I don't know' if the information is not present in the provided source text, developers can significantly improve the factual reliability of the system. This practice forces the model to prioritize provided data over its internal training parameters, improving accuracy in domain-specific tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Setting the temperature parameter to 0.
Why it's wrong here
While a temperature of 0 makes the model more deterministic, it does not inherently prevent hallucinations. The model might still confidently generate incorrect information if the prompt is ambiguous or if the context is insufficient. Lowering temperature is a secondary optimization, not a primary solution for factual grounding.
- ✓
Instructing the model to admit when it lacks information.
Why this is correct
Instructing the model to refrain from guessing and to indicate when information is missing is a highly effective grounding strategy. This creates a safety boundary, preventing the model from inventing facts when the source material is inadequate, which is essential for maintaining trust and accuracy in enterprise applications.
- ✗
Using the most expensive model variant.
Why it's wrong here
Model capacity does not equate to factual grounding. Even the most powerful models can hallucinate if they are not properly prompted or if the context is missing. Relying solely on model size ignores the fundamental importance of prompt engineering and structured inputs in ensuring reliable, verifiable model outputs.
- ✗
Increasing the max_tokens limit.
Why it's wrong here
Increasing the token limit only allows the model to generate longer responses; it does not change the model's reasoning process or accuracy. If anything, longer responses may increase the surface area for hallucinations if the model is not properly constrained through effective system prompts and source material.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
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