Question 40
A coordinator delegates data cleaning to a tool-enabled worker and asks a separate worker to review the result. What should the reviewer consume?
Explanation
Correct Answer
A separate reviewer should inspect the actual output of the cleaning operation, not merely the producing worker's narrative. The transformed dataset and a versioned change log allow row counts, field conversions, missing values and unexpected deletions to be checked against the original. Validation outcomes should identify the rules tested and any failures, with reproducible references to the exact input and output revisions. A representative sample may help investigate specific anomalies but must not replace full integrity checks where the acceptance contract requires them. This makes review an evidence-based examination of the completed transformation rather than accepting a worker's self-report.
Incorrect Answers
The original dataset alone provides a baseline, but it does not tell the reviewer which rows the cleaning worker changed, deleted or newly created. Without a transformed output or diff, the reviewer cannot establish whether input normalization was applied consistently or caused data loss. It should request the actual resulting dataset, a reproducible change record and validation summaries tied to the original revision. Reviewing only the input cannot certify the mutation that the task was supposed to perform.
The producer's claim that cleaning succeeded cannot reveal which source rows changed or whether any records were dropped. A model may sound confident even when its transformation script skipped a file or misconverted a field. The reviewer needs independent artifacts: actual cleaned data, counts or diffs, validation results and a stable link to the input revision. Success prose may help explain intent but cannot establish the transformed dataset's accuracy or completeness.
A producer-selected sample may illustrate a transformation but cannot establish that the entire dataset was cleaned correctly. It could omit the very rows most vulnerable to an accidental deletion or incorrect conversion. The reviewer needs the actual transformed dataset or a reproducible diff, validation outcomes and traceability to the original inputs. A sample can complement that evidence, but should not replace systematic row-level and risk-sensitive checks.



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