CCDV-F Model Selection and Cost Management Practice Question
A developer runs a nightly batch job that sends 8,000 independent product-description summarization requests to the Anthropic API. Each request shares an identical 12,000-token instruction block and a unique 300-token product description. The team wants to reduce input-token spend without changing output quality or model. Which approach best meets this goal?
⚠ Common exam trap
The trap here is assuming cost reduction always means shortening the prompt or downgrading the model, rather than reusing an unchanged large prefix through prompt caching.
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
✓
Use prompt caching with cache_control on the shared 12,000-token instruction block and place it at the beginning of each request.
The dominant cost is the repeated 12,000-token instruction block, not the small per-product text. Prompt caching is designed for exactly this pattern: a stable, large prefix reused across many calls. Marking that block with cache_control lets later requests read it at the reduced cache-read rate, while the variable 300-token description remains uncached. This reduces spend without changing the model, the instructions, or the output quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use prompt caching with cache_control on the shared 12,000-token instruction block and place it at the beginning of each request.
Why this is correct
Prompt caching rewards a stable prefix: the 12,000-token instruction block is identical across all 8,000 calls, so marking it with cache_control lets subsequent requests read it from cache at the reduced cache-read rate instead of paying full input price every time. The unique 300-token description stays outside the cached prefix, preserving correctness while cutting the dominant cost.
- ✗
Send all 8,000 product descriptions in a single request so the instruction block is transmitted only once.
Why it's wrong here
Batching thousands of independent items into one request risks exceeding context limits, produces interleaved outputs that are hard to map back to products, and still bills the full instruction block once per call. It does not provide the incremental per-request input savings that caching the shared prefix delivers, and it complicates error handling and retries.
- ✗
Switch the batch job to a smaller, cheaper model for all 8,000 requests.
Why it's wrong here
Changing models may reduce token prices, but the requirement explicitly states the team must not change the model or output quality. A smaller model can produce different summaries and may not follow the detailed instruction block as reliably. This option solves cost by altering the solution's behavior, which violates the stated constraint of the scenario.
- ✗
Reduce the shared instruction block to 2,000 tokens so that every request costs less per call.
Why it's wrong here
Shrinking the instruction block lowers per-call input tokens, but it changes the prompt content and therefore risks degrading summary quality and consistency. The scenario requires preserving output quality, so altering the instructions is not an acceptable cost strategy. It also misses the larger structural win available from caching the unchanged block across thousands of identical requests.
About these practice questions
This CCDV-F question is part of Courseiva's 257-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.