CCAR-P Governance, Safety, and Risk Management Practice Question
When deploying Claude in a production environment, an architect notices that the model occasionally generates responses that are slightly biased. What is the most appropriate governance-first approach to address this?
⚠ Common exam trap
Candidates often select 'retraining the model' or 'fine-tuning' as the solution. These are expensive, slow, and overkill for addressing occasional bias, which is better managed through prompt engineering and systematic monitoring.
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 a system prompt to define neutral behavior and implement bias-detection evals.
Bias in AI is an ongoing challenge that requires active management. A governance-first approach involves using a combination of model-native features, like system prompts, and external evaluation frameworks to measure and mitigate bias consistently across the application's lifecycle, rather than ignoring the problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore the bias as long as the model's overall accuracy remains high.
Why it's wrong here
Ignoring bias is a major governance failure that can lead to reputational damage and unfair user experiences. Even if a model is 'accurate' on average, specific biased outputs can alienate users and violate corporate social responsibility and ethical AI standards.
- ✗
Switch to a smaller model version to reduce the complexity of the outputs.
Why it's wrong here
Smaller models are not inherently less biased than larger ones; in fact, they may sometimes exhibit more bias due to less comprehensive safety training. Reducing model size is a performance or cost optimization, not a valid strategy for addressing fundamental safety or ethical issues.
- ✓
Use a system prompt to define neutral behavior and implement bias-detection evals.
Why this is correct
System prompts can explicitly instruct the model to be objective and neutral. By pairing this with 'evals' (automated tests that measure bias in responses), an architect can create a feedback loop that continuously monitors and improves the model's adherence to fairness standards.
- ✗
Manually rewrite every biased response before it reaches the end user.
Why it's wrong here
Manual rewriting is not scalable for production applications with high traffic. While human-in-the-loop is valuable for auditing, a sustainable governance strategy must rely on automated guardrails and principled prompt engineering to handle the volume of requests efficiently while maintaining safety.
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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 CCAR-P 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-P exam.