CCAR-F Claude Code Configuration and Workflows Practice Question
Exhibit
{
"model": "claude-3-5-sonnet-20241022",
"temperature": 0.2,
"max_tokens": 8192
}Refer to the exhibit. A developer is experiencing slow response times during complex refactoring tasks. Based on the provided configuration, what is the most likely cause of performance issues?
⚠ Common exam trap
Candidates often blame network latency or API server load, overlooking that a high max_tokens setting forces the model to generate more content, directly increasing the total request processing time.
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
✓
The max_tokens setting is causing extended generation times.
The configured max_tokens value of 8192 is significantly high, which directly impacts the latency of the API responses. During complex refactoring, if the model attempts to generate very long sequences, the time-to-first-token and total generation time increase. Reducing this value to a more constrained scope ensures faster turnaround times while still allowing sufficient room for code generation, optimizing the developer's workflow during iterative coding sessions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The temperature of 0.2 is too high for code generation.
Why it's wrong here
A temperature of 0.2 is actually quite low and generally preferred for deterministic code generation tasks where precision is key. Increasing this would add more randomness, which is the opposite of what is typically desired for stable refactoring, so this is not the cause of the slow performance.
- ✗
The model version is experimental and suffers from high latency.
Why it's wrong here
The model specified is the stable Claude 3.5 Sonnet release, which is optimized for high-performance coding tasks. It is not an experimental model and is widely used for production-grade development workflows, making it an unlikely candidate for causing performance bottlenecks during standard code refactoring operations.
- ✓
The max_tokens setting is causing extended generation times.
Why this is correct
Requesting a very large max_tokens limit forces the model to potentially generate long outputs. In the context of coding, excessive generation without proper modularization increases latency. By lowering this limit, the developer can encourage the agent to produce more concise, targeted responses, improving overall tool responsiveness during complex tasks.
- ✗
The configuration is missing the system_prompt field.
Why it's wrong here
A missing system prompt would impact the behavior and style of the model's output rather than the raw latency of the API response. The configuration schema shown is valid for basic tool operation, and the lack of a system prompt does not account for the observed performance degradation.
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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-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.