Which combination best represents standard data quality practices?

• A. Data profiling and cleansing.
• B. Data profiling and encryption.
• C. Cleansing and compression.
• D. Profiling and archiving.

Answer: (A)

The main idea is that maintaining data quality relies on both understanding and improving it. Data profiling examines the data to uncover issues like duplicates, missing values, inconsistencies, and anomalies. Once these issues are identified, data cleansing fixes them by correcting errors, standardizing formats, removing duplicates, and filling in gaps. This two-step approach—assessing quality and then improving it—is a standard data quality practice because it directly targets the accuracy, completeness, and consistency of data.

Encryption protects confidentiality and is not a data quality activity, so it doesn’t belong in the standard data quality set. Compression focuses on storage efficiency rather than data quality. Archiving moves data to long-term retention rather than actively improving current data quality. So the combination of profiling and cleansing best represents standard data quality practices.