CCAR-P Practice Question: Stakeholder Communication and Lifecycle Management
Exhibit
{
"model": "claude-3-5-sonnet-20240620",
"max_tokens": 1024,
"system": "You are a financial analyst. Provide concise, bulleted summaries.",
"messages": [
{"role": "user", "content": "Analyze the Q3 report."}
]
}Refer to the exhibit. A stakeholder reports that the output is too verbose. Which change would best address the stakeholder requirement while maintaining model performance and architectural standards?
⚠ Common exam trap
Candidates mistakenly suggest changing the model architecture or temperature instead of directly modifying the system prompt for behavioral constraints.
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
✓
Update the system prompt to explicitly state: 'You are a financial analyst. Provide only bulleted summaries. Do not exceed 100 tokens per response.'
Optimizing the system prompt is the most efficient way to influence model behavior without changing the underlying architecture or model version. By explicitly constraining the output format within the system instruction, you provide a clear boundary for the model. This satisfies the stakeholder's request for brevity while demonstrating proactive lifecycle management and iterative refinement of the AI's utility to the business.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace 'claude-3-5-sonnet-20240620' with 'claude-3-haiku-20240307'.
Why it's wrong here
Switching models is an architectural decision that should be based on performance benchmarks, not just formatting preference. While Haiku is faster, it may not possess the reasoning depth required for financial analysis. The requested issue (verbosity) is a prompt-level configuration concern, not a model capability deficit that necessitates a switch.
- ✓
Update the system prompt to explicitly state: 'You are a financial analyst. Provide only bulleted summaries. Do not exceed 100 tokens per response.'
Why this is correct
Updating the system prompt is the standard method for enforcing output constraints. By adding specific length and formatting requirements, you directly address the stakeholder feedback. This change is low-risk, easily reversible, and clearly communicates the expected behavior to the model, ensuring consistent results without requiring code-level infrastructure changes.
- ✗
Hard-code a truncation script in the application layer to cut off responses after 500 characters.
Why it's wrong here
Application-layer truncation is a poor architectural practice as it may cut off the model mid-sentence, resulting in incomplete and non-sensical information for the user. It is always better to prompt the model to be concise than to force a technical truncation that destroys the utility of the response.
- ✗
Advise the stakeholders that verbosity is a byproduct of the model's intelligence and cannot be altered.
Why it's wrong here
Dismissing stakeholder feedback is unprofessional and demonstrates a lack of control over the AI lifecycle. Verbosity is a manageable parameter. As an architect, you are responsible for tuning the system to meet business needs, and claiming that the model is uncontrollable undermines your role in governing the technology.
About these practice questions
Courseiva writes every CCAR-P question from scratch — 262 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-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.