CCAR-F Prompt Engineering and Structured Output Practice Question
You are building a system that must extract entities from messy transcripts. The model often misses entities at the end of the text. What is the most likely cause and architectural fix?
⚠ Common exam trap
Candidates often suggest increasing the model's temperature or changing the model version, failing to identify that the issue is a structural 'recency bias' inherent in long-context processing.
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
✓
Place the instructions before the transcript and repeat them after the transcript.
The model is likely experiencing 'recency bias' or 'middle-of-the-prompt' degradation, where focus shifts away from the end. By moving instructions to the end of the prompt or using 'sandwich prompting'—placing key instructions at both the start and end—you reinforce the task. This architectural adjustment ensures the model maintains focus on the entire context window, which is vital for long-context entity extraction tasks in production environments.
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 context window is too small; increase it by reducing the prompt complexity.
Why it's wrong here
Claude's context window is large enough for most transcripts. The issue is usually not the window size but the model's prioritization of data. Reducing prompt complexity does not improve attention to detail at the end of the document, as the core issue is instruction focus, not memory capacity.
- ✓
Place the instructions before the transcript and repeat them after the transcript.
Why this is correct
Reinforcing instructions at the end of a long context window is a proven technique to counter degradation. By providing the instruction again after the transcript, you anchor the model's focus, ensuring it processes the final elements of the input with the same level of care as the start.
- ✗
Switch to a smaller model to increase processing speed and focus.
Why it's wrong here
Smaller models typically have less reasoning capability and capacity for long-context tasks. Switching to a smaller model will likely exacerbate the issue of missing entities, as it reduces the model's ability to maintain coherent focus over complex, lengthy transcripts rather than solving the underlying attention problem.
- ✗
Set the top_p parameter to 0.0 to force deterministic entity extraction.
Why it's wrong here
Top_p affects sampling diversity, not the model's attention to specific sections of the input. Adjusting sampling parameters will not resolve issues related to context window performance or instruction following at the end of a document. The issue is structural, not related to the sampling probability distribution.
About these practice questions
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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.