CCAR-P Governance, Safety, and Risk Management Practice Question
Exhibit
{"model": "claude-3-5-sonnet-20240620", "max_tokens": 4096, "temperature": 0, "stop_sequences": ["Human:"]}Refer to the exhibit. An organization uses this configuration to prevent the model from continuing the conversation as the user. What is the governance benefit of this configuration?
⚠ Common exam trap
Test-takers often confuse stop sequences with content filtering or token truncation for cost management, missing their role in preventing user impersonation and jailbreaks.
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
✓
It prevents the model from generating text as the user, reducing injection risk.
Setting the stop sequence to 'Human:' prevents the model from generating text that mimics the user's voice, which is a common technique in jailbreaking and prompt injection. By forcing the model to stop, the organization maintains clear control over the conversational flow, ensuring that the model cannot 'speak' for the user or inadvertently generate unintended content that might mislead other systems or bypass security filters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It significantly reduces the total cost of the API call by limiting output length.
Why it's wrong here
While stop sequences limit the length of a specific response, they are not primarily cost-saving tools. Their main purpose in governance is to enforce structural boundaries and prevent the model from assuming the user's identity, which is a critical security and behavioral constraint for multi-turn interactions.
- ✓
It prevents the model from generating text as the user, reducing injection risk.
Why this is correct
Stop sequences are a critical defense against models hallucinating further user turns. By forcing the model to stop at the 'Human:' token, the system prevents the model from generating its own prompts, which is a standard vector for prompt injection and conversation hijacking attacks in LLM applications.
- ✗
It improves the creativity of the model by forcing it to summarize more frequently.
Why it's wrong here
Stop sequences have no direct impact on model creativity or summarization capability. Their influence is strictly on the termination of the output generation process, not on the qualitative content of the model's response. This is a common misconception about the role of stop tokens in LLM engineering.
- ✗
It allows the model to handle more complex logic by processing it in smaller chunks.
Why it's wrong here
Stop sequences do not break up or simplify logical processing. They simply tell the API to stop generating once a specific string is reached. This is a configuration control for behavioral stability, not a mechanism for enhancing the underlying logical reasoning capabilities of the model itself.
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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.