CCDV-F Claude API Mechanics Practice Question
A developer sends a Messages API request that includes a system prompt and several prior turns, and Claude replies with stop_reason set to "max_tokens" even though the conversation is short. They want Claude to finish its thought rather than truncate. Which change most directly addresses the truncation?
⚠ Common exam trap
The trap here is treating truncation as a prompt-quality or determinism problem when the stop_reason explicitly identifies the output token ceiling as the cause.
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
✓
Increase the max_tokens value in the request so Claude has more room to complete its response.
The stop_reason value max_tokens is a direct signal that the output token limit was reached before the model finished. The remedy is to raise max_tokens within the model's context constraints; stop sequences, temperature, and prompt placement do not affect how many output tokens are permitted.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Move the instructions from the system prompt into the first user turn to free output tokens.
Why it's wrong here
Where instructions live affects the input prompt, not the output token budget. Relocating them does not increase the allowed completion length, so the model would still stop at the same max_tokens boundary and the truncation would persist.
- ✗
Lower the temperature to 0 so the response becomes deterministic and naturally shorter.
Why it's wrong here
Temperature controls sampling randomness, not the output length limit. A temperature of 0 makes the answer more deterministic but does nothing to raise the token ceiling, so a response that already hit max_tokens would still be truncated at the same point.
- ✗
Add a stop_sequence that matches the end of the expected answer so Claude knows when to stop.
Why it's wrong here
Stop sequences cause generation to halt when the model emits the matching string; they never extend output. Adding one would make truncation more likely, not less, and would not address a max_tokens stop because the model was already cut off before producing the sequence.
- ✓
Increase the max_tokens value in the request so Claude has more room to complete its response.
Why this is correct
A stop_reason of max_tokens means generation halted because the output token ceiling was reached, not because Claude finished. Raising max_tokens gives the model additional output budget so it can complete the response, and the developer should also confirm the value does not exceed the model's remaining context window.
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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 CCDV-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 CCDV-F exam.