CCAR-F Prompt Engineering and Structured Output Practice Question
To reduce the risk of Claude hallucinating information when it doesn't know the answer, what instruction should be added to the prompt?
⚠ Common exam trap
Candidates tend to choose generic system instructions like 'always tell the truth' instead of specifically instructing the model to output a precise fallback phrase like 'I don't know' when uncertain.
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
✓
Tell the model to say 'I don't know' if unsure.
Explicitly giving the model an 'out' is a highly effective way to prevent hallucinations. By instructing Claude to say 'I don't know' or 'Information not found' when it is uncertain, you steer the model away from its natural tendency to be helpful by generating plausible but incorrect answers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Tell the model to 'Be as creative as possible'.
Why it's wrong here
Encouraging creativity is the opposite of what is needed to prevent hallucinations. In a factual or data-driven context, a 'creative' model is much more likely to invent details or bridge gaps in its knowledge with fabricated information that sounds convincing but is actually false.
- ✗
Instruct the model to 'Always provide an answer'.
Why it's wrong here
Forcing the model to always provide an answer is a primary cause of hallucinations. If the information is not in the context or the model's training data, this instruction compels the model to make something up to satisfy the user's demand for a response.
- ✓
Tell the model to say 'I don't know' if unsure.
Why this is correct
This simple instruction sets a clear behavioral boundary. It gives the model permission to be honest about its limitations, which significantly increases the reliability of the system by ensuring that the model only provides information it can actually verify from the provided source or training.
- ✗
Set the max_tokens to a very high number.
Why it's wrong here
Max tokens only controls the length of the response and has no impact on the accuracy of the information. A model can hallucinate just as easily in a short response as it can in a long one, so this parameter is irrelevant for mitigating hallucination risks.
About these practice questions
Courseiva writes every CCAR-F question from scratch — 271 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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
This CCAR-F practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCAR-F exam.