CCAO-F Safety and Responsible Use Practice Question
When Claude provides a response that is factually incorrect but delivered with high confidence, this is known as a hallucination. How does Anthropic's 'Honest' pillar address this issue during model training?
⚠ Common exam trap
Candidates often think hallucinations are solved by increasing model parameters or scaling context windows, rather than specifically training the model to express uncertainty.
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
✓
By training the model to express uncertainty and refuse to answer if unsure.
The 'Honest' pillar aims to make the model's confidence levels match its actual accuracy. During training, Claude is encouraged to admit when it is uncertain or doesn't have enough information to answer. This reduces the frequency of hallucinations and ensures the model is more transparent about its own limitations to the user.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
By forcing the model to cite a source for every single word it generates.
Why it's wrong here
While citations help with verifiability, forcing a source for every word is impractical and would degrade the model's fluency. The Honesty pillar focus is on general factual accuracy and the model's ability to express appropriate levels of certainty, rather than a rigid requirement for word-level citations in every response.
- ✗
By training the model to prioritize being polite over being factually correct.
Why it's wrong here
Prioritizing politeness over accuracy would actually violate the Honesty pillar. While Claude is trained to be helpful (which includes being polite), the Honesty pillar specifically mandates that the model should not provide false information, even if doing so would seem more 'agreeable' or helpful to the user.
- ✓
By training the model to express uncertainty and refuse to answer if unsure.
Why this is correct
Anthropic uses RLHF to reward the model for saying 'I don't know' when it lacks sufficient information. This alignment helps the model avoid making up facts to satisfy a user's prompt. A truly honest AI is one that understands and communicates the boundaries of its own knowledge effectively.
- ✗
By connecting the model to a real-time truth-checking database for every query.
Why it's wrong here
While models can be used with retrieval systems (RAG), the 'Honest' pillar refers to the model's intrinsic training and behavior. A base model is not constantly connected to a live database during its standard inference; its honesty must be an inherent quality developed during the training and alignment phases.
About these practice questions
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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
This CCAO-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 CCAO-F exam.