CCDV-F Claude API Mechanics Practice Question
An application needs to ensure that Claude stops generating text as soon as it produces a specific character sequence, such as 'END_OF_REPORT'. Which API feature should be used to implement this behavior?
⚠ Common exam trap
Candidates often confuse stop sequences with max_tokens or attempt to instruct Claude via system prompts to stop generating, which is less reliable.
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 'stop_sequences' top-level parameter.
Stop sequences are a fundamental tool for controlling the termination of Claude's output. By providing a list of strings, developers can force the model to cease generation immediately upon producing those strings. This is vital for maintaining the structure of generated documents and ensuring that the model does not continue into unwanted or redundant text.
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 'stop_sequences' top-level parameter.
Why this is correct
The stop_sequences parameter allows you to define up to 20 custom strings that will signal Claude to stop generating. When the model generates any of these sequences, it terminates the response immediately. The sequence itself is not included in the final output, making it perfect for clean text termination.
- ✗
Setting 'max_tokens' to the exact length of the expected report.
Why it's wrong here
Setting max_tokens is an imprecise way to stop generation because token lengths vary based on content and formatting. It will cut off the model regardless of whether the sentence is complete, often leading to fragmented and low-quality results rather than a clean, logical stop at a specific marker.
- ✗
Using a 'system' prompt to tell Claude to stop at 'END_OF_REPORT'.
Why it's wrong here
While Claude might follow this instruction, it is not a guaranteed programmatic stop. The model may occasionally forget the instruction or hallucinate a reason to continue. The stop_sequences parameter is a hard constraint enforced by the API's sampling logic, making it far more reliable for production applications.
- ✗
Setting 'temperature' to 0 to make the output more predictable.
Why it's wrong here
Temperature controls the randomness of the token selection, not where the model decides to stop its generation. While a lower temperature makes the model more likely to follow a specific pattern, it does not provide a mechanism for the API to recognize and terminate at a specific string sequence.
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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.