CCAR-F Prompt Engineering and Structured Output Practice Question
Exhibit
Error: Output truncated due to token limit. Current schema violation: missing closing brace.
Refer to the exhibit. Your API call is returning incomplete JSON because it hits a token limit. What is the most robust architectural fix for this error?
⚠ Common exam trap
Test-takers frequently select answers that suggest simply increasing the maximum output token limit, overlooking the architectural instability of generating massive monolithic responses.
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
✓
Decompose the data extraction into multiple, smaller requests to avoid hitting limits.
Truncation at the token limit is a structural failure. Simply increasing the token limit is a temporary fix that can still fail with larger inputs. The robust solution is to architect the application to return smaller, modular pieces of data that the model can generate within the limits, or to use a streaming implementation that can handle multi-part responses, ensuring each piece is valid and complete.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the max_tokens limit to handle the full JSON response.
Why it's wrong here
Increasing the limit is a reactive patch that only delays the problem. If the task requires more data than the token limit allows, it will always eventually fail. Architectural changes that break the task down are much more reliable than simply trying to expand the output buffer of the API.
- ✗
Rewrite the system prompt to force the model to be more concise and avoid truncation.
Why it's wrong here
While conciseness helps, you cannot force the model to be 'more concise' if the task requires a large output. If the necessary information exceeds the limits, it will still get truncated. You need to manage the task scope, not just ask the model to be brief, to ensure success.
- ✓
Decompose the data extraction into multiple, smaller requests to avoid hitting limits.
Why this is correct
Decomposition is the correct architectural approach. By breaking the task into parts, you ensure each request finishes within the token limit, yielding valid, fully-formed JSON for each chunk. This makes the system more resilient and avoids the structural failures caused by attempting to force large data in one turn.
- ✗
Use a post-processing script to append the missing closing brace.
Why it's wrong here
Adding a closing brace is a hack that does not address the fact that the content itself was likely truncated as well. The data is incomplete, not just the JSON structure. A hacky fix like this results in silently corrupted data, which is far worse than an explicit error.
About these practice questions
One of 271 original CCAR-F practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-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.