CCDV-F Prompt and Context Engineering Practice Question
A developer is building a pipeline that summarizes a 200-page technical manual with Claude Sonnet. The manual is too long for a single request, so the developer splits it into 40 chunks and summarizes each chunk independently. The final summaries must remain factually consistent with one another and with the source, and the developer has a fixed token budget. Which two techniques should the developer apply to keep the chunk summaries consistent and grounded? (Choose two.)
⚠ Common exam trap
The trap here is assuming that deterministic sampling settings or repeated passes can substitute for actually sharing information between independent chunk requests.
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
✓
Carry a running 'state' summary of key entities, definitions, and decisions forward into each subsequent chunk request.
Cross-chunk consistency requires either shared context or a reconciliation step. Carrying a running state forward gives each request the key facts established earlier, while a verification pass that reconciles adjacent summaries catches contradictions before they propagate. Together they maintain grounding and consistency within a bounded token budget, unlike duplicating output or resending all prior 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.
- ✗
Include the full text of all previously processed chunks in every new request.
Why it's wrong here
Including all prior chunks recreates the original context-length problem and will exceed the context window well before the manual is finished. It also inflates cost quadratically as the pipeline progresses. The developer explicitly has a fixed token budget, so this approach is both technically infeasible and economically unacceptable for a 200-page document.
- ✗
Ask the model to summarize each chunk twice and concatenate both outputs to increase coverage.
Why it's wrong here
Summarizing twice and concatenating doubles output tokens and often produces redundant or conflicting statements, which worsens consistency rather than improving it. Duplication does not create cross-chunk awareness, so a term defined in chunk 3 still has no way to influence chunk 27. It also consumes the fixed token budget without adding grounding.
- ✗
Lower the temperature to zero and rely on deterministic sampling to guarantee identical facts across chunks.
Why it's wrong here
Temperature zero reduces randomness but cannot guarantee factual consistency across chunks, because each chunk is processed with different input text and no shared memory. Determinism affects sampling variance, not information flow between calls. Facts absent from a given chunk's input will still be absent from its summary regardless of temperature.
- ✓
Carry a running 'state' summary of key entities, definitions, and decisions forward into each subsequent chunk request.
Why this is correct
Passing a compact running state (entities, defined terms, decisions) into each chunk request gives the model continuity across independent calls. Without it, each chunk is summarized in isolation and terminology or facts can drift. It costs a small, bounded number of tokens per request and directly addresses cross-chunk consistency, which is the stated requirement.
- ✓
Add a verification pass that compares each new summary against the immediately preceding summary and asks the model to reconcile differences.
Why this is correct
A verification pass that reconciles each new summary with the previous one catches drift early, before it compounds across 40 chunks. It is bounded in cost because it only compares adjacent summaries, and it leverages the model's ability to detect contradictions. This directly supports factual consistency across independently generated summaries.
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.