Which statement best describes verification in data cleansing?

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Verification in data cleansing focuses on ensuring the data you end up with after cleaning is accurate and trustworthy. After cleaning, you confirm that the fixes you applied actually result in correct values and that the dataset meets quality expectations through checks like comparing samples to an authoritative source, applying business rules, or running sanity checks on ranges and relationships. This step is about the reliability of the cleaned data, not just finding issues or validating formats earlier in the process. The other ideas describe different parts of data quality work: spotting and correcting errors before cleaning relates to remediation, validating format against a schema is about intake validation, and reviewing visualizations is about data analysis and interpretation, not confirming data quality post-cleaning.