What types of reports are typically used to confirm data quality?

The correct choice highlights the importance of exception reports and automated error reports in ensuring data quality. Exception reports are specifically designed to identify data points that deviate from expected norms or thresholds. They provide a focused view of issues within a dataset, allowing data managers to promptly address and rectify inconsistencies or errors. Automated error reports complement this by utilizing software tools to detect errors as data is being collected or processed. This automation minimizes human oversight, increases efficiency, and ensures that data is continuously monitored for quality.

In contrast, annual reports and project proposals typically serve more for strategic planning and summarization of past performances rather than directly verifying data quality. Although summary reports and presentation slides may consolidate information, they do not serve the specific purpose of identifying or confirming data integrity issues. Statistical reports and feedback forms can provide valuable insights but aren’t primarily focused on data quality verification in the same direct and proactive manner as exception and automated error reports.