CCAR-P Practice Question: Developer Productivity and Operational Enablement
Exhibit
{
"max_tokens": 4096,
"system": "You are a helpful assistant.",
"messages": [
{"role": "user", "content": "Summarize this text: [LONG_TEXT]"}
]
}Refer to the exhibit. The developer reports that the model is cutting off summaries for very long inputs. What is the most likely cause?
⚠ Common exam trap
Candidates frequently confuse the input context window limit with the max_tokens parameter, incorrectly adjusting input lengths when dealing with truncated output summaries.
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 too low for the requested output length.
The 'max_tokens' limit dictates the maximum size of the generated completion, not the input size. If the generated summary exceeds this limit, the output will be truncated. To fix this, developers must ensure the 'max_tokens' parameter is sufficiently large to accommodate the desired output length. Understanding the interaction between token limits and output requirements is crucial for operational stability in LLM-based text summarization tasks.
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 input text exceeds the model's total context window size.
Why it's wrong here
If the input exceeded the context window, the API would return a context length error before processing. The issue described is a truncation of the output, which happens when the generation limit set by 'max_tokens' is reached during the streaming or completion process for the response.
- ✓
The max_tokens setting is too low for the requested output length.
Why this is correct
The 'max_tokens' parameter constrains the number of tokens the model generates in its response. If the expected summary is longer than this value, the model will stop generating mid-sentence once the limit is hit. Increasing this value ensures that the model has enough budget to complete the summary fully.
- ✗
The system prompt is too short to handle long-form summarization.
Why it's wrong here
System prompts define behavior but do not control the token output limits. A short system prompt may affect the quality of the summary, but it does not cause premature truncation of the generated output. Truncation is exclusively a function of the 'max_tokens' configuration parameter and the generation process.
- ✗
The model version being used does not support large outputs.
Why it's wrong here
Anthropic models support large output token counts, provided the 'max_tokens' parameter is configured correctly. If the model were incapable of long outputs, it would be a fundamental limitation of the model, not a configuration issue. The current symptom points directly to an insufficient token budget allocation for the request.
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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.