Which of the following is a common data quality issue in the Large Data Set?

• A. Missing values, duplicates, inconsistent units, incorrect formats, and miscodings.
• B. Visual trends in charts.
• C. The color palette used in graphs.
• D. Document versioning.

Answer: (A)

The idea being tested is recognizing the real problems that undermine data quality in large data sets. Data quality issues like missing values, duplicates, inconsistent units, incorrect formats, and miscodings are exactly the kinds of defects that can distort analyses. Missing values leave gaps that bias results; duplicates inflate counts and distort summaries; inconsistent units can make numbers incomparable; incorrect formats can break parsing and calculations; miscodings mislabel data, leading to wrong groupings or conclusions. This option lists genuine data quality problems you’d need to detect and fix before analysis. The other options describe things unrelated to data integrity—visual trends in charts relate to interpretation, color choices affect presentation, and document versioning deals with governance—so they don’t address the actual data quality issues.