CCDV-F Prompt and Context Engineering Practice Question
A developer maintains a Claude-powered assistant that answers questions over a 60,000-token internal policy corpus. The corpus is stable and reused across every request, and the developer wants to cut cost and latency while preserving answer quality. Which two changes will most directly reduce per-request token processing for this workload? (Choose two.)
⚠ Common exam trap
The trap here is assuming that behavioral instructions or cosmetic reformatting change token accounting, when only caching and context reduction actually lower per-request 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
✓
Retrieve only the policy sections relevant to each question and place those sections in the prompt instead of the full corpus.
Prompt caching avoids reprocessing the unchanged corpus prefix, and retrieval shrinks the context to only the passages a given question needs. Both act directly on the number of tokens the model must process per request, and both preserve quality because the relevant policy content remains available to the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Raise the temperature so Claude explores a wider range of possible answers and settles faster.
Why it's wrong here
Temperature controls sampling randomness, not the number of tokens processed. Raising it makes outputs less deterministic and can harm answer quality on policy questions, while doing nothing to reduce the token volume of the 60,000-token corpus that must still be read on every request.
- ✓
Retrieve only the policy sections relevant to each question and place those sections in the prompt instead of the full corpus.
Why this is correct
Retrieval narrows the context to the passages needed for the specific question, so the model processes far fewer tokens per request. This directly lowers per-request token volume while preserving quality, because the answer-relevant content is still present in the prompt.
- ✗
Add a system instruction telling Claude to read the corpus more quickly to save tokens.
Why it's wrong here
Instructions do not change how the model tokenizes or processes input; the full corpus is still encoded and attended to regardless of wording. Such an instruction consumes tokens itself and cannot reduce the cost of the corpus, so it fails to deliver the requested reduction.
- ✗
Convert the corpus to a single long paragraph with no headings to reduce whitespace tokens.
Why it's wrong here
Whitespace and headings are a negligible fraction of a 60,000-token corpus, and removing structure makes it harder for Claude to locate relevant policy text. The token savings are trivial while answer quality can degrade, so this does not meaningfully reduce per-request processing.
- ✓
Enable prompt caching on the stable policy corpus so repeated prefixes are read from cache instead of reprocessed.
Why this is correct
Prompt caching stores the processed prefix so subsequent requests reuse it, cutting the compute and latency associated with re-reading the unchanged 60,000-token corpus. Because the corpus is stable and reused every request, it is an ideal cache prefix, making this a direct reduction in per-request token processing.
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 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.