CCDV-F Model Selection and Cost Management Practice Question
A developer is optimizing a Claude-powered document processing pipeline that sends large, mostly identical legal templates followed by short variable fields. They want to reduce input token costs while preserving output fidelity. Which TWO strategies are appropriate? (Choose two.)
⚠ Common exam trap
The trap here is treating Prompt Caching as independent of prompt structure, when cache hits actually depend on keeping the stable prefix byte-identical and placing volatile content after it.
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
✓
Enable Prompt Caching on the static legal template so repeated requests reuse the cached prefix.
The two effective strategies are caching the static template and ordering the prompt so the stable prefix comes first. Caching reduces the billed rate for the large repeated content, and correct ordering ensures the cache key remains valid across requests. Together they cut input token costs while leaving the template content and output quality intact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Prompt Caching on the static legal template so repeated requests reuse the cached prefix.
Why this is correct
The legal template is large and mostly identical across requests, making it an ideal candidate for Prompt Caching. Cache reads are billed at a reduced rate, and the template content remains unchanged, so output fidelity is preserved. This directly targets the largest repeated input component and is a standard cost optimization for template-heavy pipelines.
- ✗
Remove the legal template entirely and rely on the model's pretrained knowledge of legal language.
Why it's wrong here
Removing the template eliminates the authoritative source content the pipeline depends on, which would degrade output accuracy and fidelity. The model's general legal knowledge cannot substitute for the specific template language required. This sacrifices the pipeline's core function and is not a valid cost optimization.
- ✗
Set max_tokens to a very low value to force the model to answer briefly.
Why it's wrong here
max_tokens controls output length, not input token cost. The scenario's concern is input token cost from large templates. Artificially capping output could truncate required content and harm fidelity, so this approach addresses the wrong cost component and risks quality regressions.
- ✗
Send only the variable fields and ask the model to reconstruct the template from memory.
Why it's wrong here
Asking the model to reconstruct a specific legal template from memory would produce inconsistent, unreliable text. The template's exact wording matters in legal contexts, and reconstruction introduces hallucination risk. This approach reduces input tokens but destroys the fidelity the pipeline requires, making it inappropriate.
- ✓
Place the variable fields at the end of the prompt after the static template to maximize cache hits.
Why this is correct
Prompt Caching keys on the exact prefix, so any variation before the static content invalidates the cache. Ordering the stable template first and the volatile fields last ensures the cached prefix remains identical across requests. This maximizes cache hit rate and therefore maximizes the cost savings from caching.
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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.