DA0-002 CompTIA Data+ Exam (2025) Free Practice Exam Questions (2026 Updated)

This question is part of the Visualization and Reporting domain, focusing on delivery methods for dashboards. The requirement for displaying financial transactions "as they are updated" implies a need for real-time updates and interactivity, which narrows down the options.

Real-time (Option A): Real-time delivery ensures the dashboard reflects the latest data as transactions are updated, meeting the requirement.

Snapshot (Option B): A snapshot provides a static view at a specific point, not suitable for ongoing updates.

Dynamic (Option C): A dynamic dashboard allows for interactivity and can be updated as data changes, complementing real-time delivery.

Static (Option D): Static dashboards don’t update automatically, making this incorrect.

Ad hoc (Option E): Ad hoc delivery is for one-time reports, not ongoing updates.

Time series (Option F): Time series refers to a data type or visualization, not a delivery method.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report or dashboard" with delivery methods Real-time and dynamic methods best support the need for updated financial transaction dashboards.

This question pertains to the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a specific requirement. The task is to show the composition of total sales across three channels, which involves showing proportions.

Pivot table (Option A): A pivot table summarizes data but isn’t a visualization; it’s more for data exploration.

Pie chart (Option B): A pie chart shows the proportion of total sales for each channel (Digital, Store, Online), which is ideal for displaying composition.

KPI card (Option C): A KPI card displays a single metric, not suitable for showing composition across multiple channels.

Box plot (Option D): A box plot shows data distribution (e.g., quartiles), not proportions.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a pie chart is best for showing the composition of totals.

This question is part of the Data Concepts and Environments domain, focusing on the purpose of data documentation tools like data dictionaries and metadata. The question compares their uses.

To gather information and resources about the data (Option A): This is too vague and not specific to a data dictionary’s purpose.

To find the content and specific attributes for a dataset (Option B): A data dictionary provides detailed definitions of data elements (e.g., field names, types, descriptions), which is more specific than metadata, which often includes broader information like creation date or source.

To find a summary of basic information about the dataset (Option C): This better describes metadata, which provides high-level summaries, not detailed attributes.

To gather information about the availability of the data (Option D): Neither a data dictionary nor metadata typically focuses on data availability.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is specifically used to find detailed attributes of a dataset.

This question pertains to the Data Governance domain, focusing on data security and access control principles. The company restricts access to the minimum needed for duties, which aligns with a specific security concept.

Active Directory (Option A): Active Directory is a tool for managing users and permissions, not a concept.

Hierarchical access (Option B): Hierarchical access implies access based on roles in a hierarchy, but it doesn’t specifically focus on minimal access.

Zero Trust (Option C): Zero Trust requires continuous verification for all access, which is broader than just minimal permissions.

Least privilege (Option D): Least privilege ensures users have only the permissions necessary for their duties, which matches the scenario.

The DA0-002 Data Governance domain includes "data privacy concepts," and least privilege is a fundamental principle for secure access control.

This question falls under the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores data in its original, unaltered form.

Data lake (Option A): A data lake stores raw, unaltered data in its native format (structured, semi-structured, or unstructured), making it the correct choice.

Data warehouse (Option B): A data warehouse stores processed, structured data, often transformed for analysis, not unaltered.

Data table (Option C): A data table is a structure within a database, not a repository, and may contain altered data.

Data factory (Option D): A data factory (e.g., Azure Data Factory) is a data integration service, not a repository for storing data.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed to store unaltered data.

This question pertains to the Visualization and Reporting domain, focusing on selecting the appropriate visualization to show a relationship between two continuous variables (ages and incomes).

Scatter plot (Option A): A scatter plot displays individual data points on two axes (age vs. income), making it ideal for showing the relationship and potential correlation between two continuous variables.

Area plot (Option B): Area plots are used for showing trends over time, not relationships between two variables.

Bar chart (Option C): Bar charts are better for categorical data comparisons, not continuous variable relationships.

Pie chart (Option D): Pie charts show proportions of a whole, not suitable for showing relationships between variables.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a scatter plot is best for showing the relationship between age and income.

This question falls under the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a specific data trend. The task is to show sales trends over time (year over year).

Line (Option A): Line charts are ideal for displaying trends over time, such as year-over-year sales, as they clearly show changes and patterns across a continuous time axis.

Donut (Option B): Donut charts show proportions or percentages of a whole, not suitable for time-based trends.

Bubble (Option C): Bubble charts display three dimensions of data (e.g., size, x-axis, y-axis), not ideal for simple time trends.

Hierarchy (Option D): Hierarchy charts (e.g., treemaps) show nested relationships, not time-based trends.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a line chart is best for time-series trends.

This question pertains to the Data Governance domain, focusing on troubleshooting and maintaining data quality in software processes. The analyst has already tried basic debugging and confirmed compatibility, so the next step involves seeking external resources.

Use the old software and preexisting code, since both were functional (Option A): Reverting to old software avoids solving the issue and may introduce other risks (e.g., security vulnerabilities).

Contact the IT department and inform them that the software has a bug (Option B): Assuming a bug without further investigation is premature, especially since compatibility was confirmed.

Escalate to the department manager and ask for assistance (Option C): Escalation is a later step after exhausting technical troubleshooting options.

Research the issue online and see if a solution is available (Option D): Researching online (e.g., forums, documentation) is a logical next step to find solutions or identify known issues, especially after local debugging fails.

The DA0-002 Data Governance domain includes "data quality control concepts," and researching online is a standard troubleshooting step to maintain data process integrity.

This question pertains to the Data Concepts and Environments domain, focusing on documentation for understanding datasets. The analyst needs variable-level details (e.g., field definitions), which points to a specific type of documentation.

Data lineage (Option A): Data lineage tracks data flow and transformations, not variable definitions.

Data dictionary (Option B): A data dictionary provides detailed information about variables (e.g., name, type, description), which is exactly what the analyst needs.

Data flow diagram (Option C): A data flow diagram shows data movement, not variable-level details.

Data versioning (Option D): Data versioning tracks changes to datasets, not variable definitions.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is the primary tool for variable-level information.

This question pertains to the Data Acquisition and Preparation domain, focusing on combining and deduplicating data using SQL. The task is to remove duplicates between two tables, meaning the analyst needs a unique set of records from both.

SELECT * FROM Employees UNION ALL SELECT * FROM Managers (Option A): UNION ALL combines all rows from both tables, including duplicates, which doesn’t meet the requirement.

SELECT * FROM Employees UNION SELECT * FROM Managers (Option B): UNION combines rows from both tables and automatically removes duplicates, providing a unique set of records, which fits the task.

SELECT * FROM Employees JOIN SELECT * FROM Managers (Option C): This syntax is incorrect; a JOIN requires an ON clause, and it wouldn’t remove duplicates.

SELECT * FROM Employees CROSS JOIN SELECT * FROM Managers (Option D): A CROSS JOIN creates a Cartesian product, resulting in all possible combinations, not removing duplicates.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and UNION is the correct SQL operation for combining tables while removing duplicates.

This question pertains to the Data Concepts and Environments domain, focusing on the content of metadata. Metadata describes data attributes, and the task is to identify what it typically includes.

Transformations (Option A): Transformations (e.g., data cleaning steps) are part of data lineage, not metadata.

Data lineage (Option B): Data lineage tracks data flow and transformations, which is related to metadata but not a direct component.

Syntax (Option C): Syntax refers to code structure, not a metadata component.

Variable types (Option D): Metadata includes information about data fields, such as variable types (e.g., integer, string), which is a standard component.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and metadata typically contains details like variable types to describe the dataset.

This question falls under the Data Acquisition and Preparation domain of CompTIA Data+ DA0-002, focusing on data manipulation techniques. The task is to combine FirstName and LastName into a single FullName field (e.g., "JohnDoe").

Concatenate (Option A): Concatenation combines two or more strings into one (e.g., using CONCAT in SQL or "+" in Python), which is the correct method to create FullName from FirstName and LastName.

Filter (Option B): Filtering selects specific rows based on conditions, not suitable for combining fields.

Join (Option C): Joining combines data from multiple tables, but the task involves manipulating data within a single table.

Group (Option D): Grouping (e.g., GROUP BY in SQL) is for aggregation, not for combining fields into a new column.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and concatenation is the standard technique for combining fields like FirstName and LastName into FullName.

This question falls under the Data Analysis domain, focusing on analytical methods for reporting. The task is to identify the causes of customer churn over three years, which involves analyzing historical data.

Inferential (Option A): Inferential statistics make predictions or generalizations about a population, not focused on identifying causes in historical data.

Descriptive (Option B): Descriptive analytics summarizes historical data to identify patterns and causes (e.g., reasons for churn), which fits the task.

Prescriptive (Option C): Prescriptive analytics provides recommendations, which goes beyond identifying causes.

Predictive (Option D): Predictive analytics forecasts future outcomes, not focused on historical causes.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and descriptive analytics is best for identifying causes in historical data.