DA0-002 Exam Dumps - CompTIA Data+ Exam (2025)

This question pertains to the Data Governance domain, focusing on data quality and consistency in survey design. Requiring all questions to have an answer ensures a specific type of data quality.

Missing values (Option A): Requiring answers prevents missing values (NULLs or blanks) in the survey responses, which is the primary inconsistency this setting addresses.

Duplication (Option B): Duplication refers to repeated records, not prevented by requiring answers.

Data corruption (Option C): Data corruption involves damaged or altered data, not related to missing answers.

Completeness (Option D): Completeness is the concept of having all necessary data, but "missing values" is the specific inconsistency prevented here.

The DA0-002 Data Governance domain includes "data quality control concepts," and preventing missing values ensures data integrity in survey responses.

This question falls under the Data Concepts and Environments domain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.

Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.

Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.

Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.

Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.

The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.

This question is part of the Data Concepts and Environments domain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.

Redundancy (Option A): The TypeID column (e.g., "Full Time 1") redundantly includes the Type value ("Full Time") with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.

Outlier (Option B): Outliers are data points that deviate significantly, which isn’t applicable here.

Missing data (Option C): There are no missing values in the table.

Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and redundancy is a common data quality issue in schema design.