Provenance & uncertainty in multi-source synthesis
Preserve information provenance and handle uncertainty in multi-source synthesis.
This subtopic (5.6) sits in Context Management & Reliability (D5) on Anthropic's Claude Certified Architect — Foundations (CCA-F) exam. The bank holds 19 practice questions here — 4 easy, 9 medium, and 6 hard — with 5 free to try, answers and explanations included. 3 of the free questions are below; the rest are in the practice stream.
What the exam tests here
- conflicting statistics from credible sources — annotate conflicts, do not arbitrate
- source attribution lost during summarization — claim-source mappings must survive
- temporal data requires publication dates to prevent misinterpretation
- structured claim-source mappings preserved through synthesis steps
Free practice questions: Provenance & uncertainty in multi-source synthesis
Your support agent calls get_customer, lookup_order, and process_refund, then produces a one-sentence summary. A downstream audit finds a disputed refund claim but cannot trace which tool response originated it. What is the most direct fix?
Show answer & explanation
Correct answer: C. Append a structured citation block to each summary mapping every claim to its source tool call and response ID.
A structured citation block embedded in the summary keeps claim-source mappings intact at generation time, surviving any downstream handoff. 'Increase the agent's context window' retains raw data in memory but does not produce auditable output. 'Log to an external datastore' moves the problem rather than solving it inline. 'Re-run get_customer' regenerates data but cannot reconstruct the original decision path.
After lookup_order returns order details from three sources, the agent synthesizes a single summary for the customer. A billing dispute later reveals the summary omitted which source flagged a partial payment. What design prevents this provenance loss?
Show answer & explanation
Correct answer: A. Return source-tagged claim objects from lookup_order so each fact carries its origin through synthesis.
Embedding source tags on each claim at the tool-output level ensures provenance survives summarization structurally, not by model compliance. 'Increase context window size' retains data but does not bind facts to origins. 'Instruct the agent via system prompt' is probabilistic and fails under compression. 'Store raw responses in a database' enables post-hoc retrieval but does not preserve claim-source mapping through synthesis.
Your team's /market-sizing slash command in .claude/commands/ synthesizes TAM estimates from analyst reports. For one segment, Gartner reports $4.2B and Forrester reports $6.8B, both from 2024. The command currently picks the higher figure and proceeds. How should you modify the command to handle such conflicts correctly?
Show answer & explanation
Correct answer: A. Surface both figures with source attribution and flag the discrepancy, leaving resolution to the human reviewer
When credible sources conflict, the agent must preserve provenance and annotate the conflict rather than arbitrate it — the human reviewer owns the judgment call. 'Average the two figures' silently fabricates a number neither source published. 'Select the more recent publication' imposes a heuristic the sources did not agree to. 'Prompt the model to reason about which methodology' asks the LLM to arbitrate between credible sources, exactly the behavior to avoid.
2 more free questions on this subtopic in the practice stream, plus 14 in the full bank. Keep practicing →