What statistics are typically collected in data quality profiling?

• A. File size and last modified date.
• B. Number of users and access rights.
• C. Color codes and font sizes of dashboards.
• D. Distributions, completeness, uniqueness, and anomalies.

Answer: (D)

In data quality profiling, you look at statistics that describe the data itself to understand its quality. The usual measures include distributions, which show how values are spread and whether any ranges dominate; completeness, which tracks missing values and how complete each field is; uniqueness, which checks for duplicate records or repeated values that can distort analysis; and anomalies, which identify values or patterns that don’t fit expected rules or behavior. Together, these statistics reveal where data quality issues exist and guide improvements.

Other options don’t fit data quality profiling because file size and last modified date describe the file as a whole rather than the data content; the number of users and access rights relate to security and governance rather than data quality; and color codes and font sizes pertain to how dashboards are presented, not to the data’s quality.