CCAO-F Question 32

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose B2

  Because the workflow has to follow the natural data flow: you can't organize or group requirements that don't exist yet. Walking through it: 1. **(4) Gather and label the sources** — first you collect and tag the raw materials so every requirement can later be traced back. Nothing can happen before you have labeled inputs. 2. **(3) Submit to Claude with a structured extraction prompt** — now you actually extract the requirements from those sources. This has to come before grouping, because you need the extracted output to group. 3. **(1) Group related requirements and flag conflicts** — once Claude has produced structured requirements, you cluster them and surface ambiguities/conflicts for stakeholders. 4. **(2) Validate against the original sources** — check the grouped/structured output back against the labeled sources for accuracy and completeness. 5. **(5) Document the final set with traceability** — finalize and record, with status and source links. So: **4 → 3 → 1 → 2 → 5 = B.** The trap is option A (4, 1, 3, 2, 5), which puts **grouping (1) before submitting to Claude (3)**. That's backwards — you can't group or flag conflicts in requirements until the extraction step has actually produced them. The "Most Voted" tag on A is just crowd voting, and it's wrong here.

• Chose A3

  Step (4) — Gather and label source materials: Why first: Before you can process, analyze, or prompt an AI with any requirements, you must first collect, organize, and label your inputs (e.g., meeting transcripts, stakeholder emails, documents). Without clear labels, you lose source traceability from the very beginning. Step (1) — Group related requirements and flag conflicts/ambiguities: Why second: Before feeding raw or unorganized text into an AI model, a human analyst needs to review the collected materials, cluster related ideas, and identify glaring contradictions or missing details. Doing this upfront ensures the inputs are structured logically and prepares clean, coherent data for the AI. Step (3) — Submit consolidated inputs to Claude with a structured extraction prompt: Why third: Now that your materials are gathered, labeled, and pre-screened for obvious conflicts, you feed them into Claude using a well-defined, structured prompt to extract, categorize, and format the requirements systematically. Step (2) — Validate the structured output against original sources: Why fourth: AI models can hallucinate, misinterpret, or miss subtle details. Therefore, immediately after receiving the structured output from Claude, you must cross-check it against the original source materials to ensure complete accuracy, fidelity, and completeness. Step (5) — Document the final requirements set with source traceability and status: Why last: Once the AI's output has been rigorously validated and any gaps or conflicts are resolved, you finalize and publish the formal requirements document complete with tracking statuses and source references.