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

This question pertains to the Visualization and Reporting domain, focusing on report refresh frequencies. The manager needs the report to always be up-to-date, implying continuous data updates.

Real-time (Option A): Real-time refresh frequency ensures the report reflects the latest data as soon as it changes, which matches the requirement to "always be up-to-date."

Ad hoc (Option B): Ad hoc reports are generated on-demand, not continuously updated.

Snapshot (Option C): A snapshot captures data at a specific point in time, not suitable for always being up-to-date.

Dynamic (Option D): Dynamic reports allow interactivity, but the term doesn’t specifically imply real-time updates.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and real-time refresh frequency ensures the report is always current.

This question pertains to the Data Analysis domain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal, e.g., ratings), requiring appropriate data attributes.

Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.

Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.

Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.

Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and numerical and ordinal attributes are key for forecasting sales and satisfaction.

This question pertains to the Data Concepts and Environments domain, focusing on documentation for understanding data processes. The analyst needs to understand automation of data components, which involves data movement and processes.

Data flow diagram (Option A): A data flow diagram (DFD) visualizes how data moves through systems, including automated processes, making it the best starting point.

Data explainability report (Option B): This is related to AI/ML model transparency, not data automation.

Data dictionary (Option C): A data dictionary defines data elements, not how they’re automated.

Data lineage (Option D): Data lineage tracks data origin and transformations but doesn’t focus on automation processes.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data flow diagram is key for visualizing automation.

This question falls under the Data Concepts and Environments domain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.

Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.

Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.

Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.

Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they’re not specifically designed for relational keys in a data warehousing context.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.

This question falls under the Visualization and Reporting domain, focusing on identifying tools used for business intelligence (BI), which typically involves data visualization and reporting.

SAS (Option A): SAS is a statistical analysis software, not primarily a BI tool focused on visualization.

Python (Option B): Python is a programming language, not a BI software, though it can be used for data analysis with libraries.

Notepad++ (Option C): Notepad++ is a text editor, not a BI tool.

Tableau (Option D): Tableau is a leading BI software designed for data visualization, dashboards, and reporting, making it the correct choice.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report or dashboard," and Tableau is a recognized BI tool for this purpose.

This question falls under the Data Acquisition and Preparation domain, focusing on modifying historical data. The task is to add new classification codes to existing data, which involves adding new rows or columns.

Append (Option A): Appending adds new rows to a dataset, which is suitable if the classification codes are new records (e.g., a new table of codes to combine with historical data). If the codes are a new column, a join or update might be used, but append fits the context of adding new data.

Binning (Option B): Binning groups data into categories, not suitable for adding classification codes.

Parsing (Option C): Parsing breaks down data (e.g., splitting strings), not relevant for adding codes.

Union (Option D): Union stacks tables with identical structures, but the task involves adding new data (codes) to historical data, not combining identical tables.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and appending is a common technique for adding new data to historical datasets.

This question pertains to the Data Concepts and Environments domain, focusing on tools for interacting with relational databases. The task is to identify the best method for retrieving and manipulating data.

XML (Option A): XML is a data format, not a language for retrieving or manipulating database data.

SQL (Option B): SQL (Structured Query Language) is specifically designed for querying and manipulating data in relational databases (e.g., SELECT, UPDATE), making it the best choice.

Excel (Option C): Excel can analyze data but isn’t designed for direct database manipulation.

JavaScript (Option D): JavaScript is a programming language for web development, not optimized for relational database operations.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases," and SQL is the standard language for relational database operations.

This question falls under the Data Governance domain of CompTIA Data+ DA0-002, focusing on troubleshooting data quality issues in dashboards. The dashboard isn’t loading properly, indicating a potential issue with the data connection or configuration.

Review the data layer and data source (Option A): The data layer (e.g., queries, connections) and data source (e.g., database) are the foundation of a dashboard. If the information isn’t loading, the issue likely lies in the data connection or query configuration, making this the first step.

Validate that the database is up-to-date (Option B): While this might be a subsequent step, it assumes the connection is working, which should be confirmed first.

Check that the program is updated to the latest version (Option C): Software updates might fix bugs, but this isn’t the most immediate cause of data not loading.

Ensure the correct filters are displaying on the dashboard (Option D): Filters affect data display, but if the data isn’t loading at all, the issue is more fundamental.

The DA0-002 Data Governance domain includes "data quality control concepts," and reviewing the data layer and source is the primary step in troubleshooting dashboard loading issues.

This question pertains to the Data Concepts and Environments domain, focusing on file formats for data sharing. The task is to identify the best reason to choose CSV over Excel for sharing data.

CSV files can store different types of encoding (Option A): While CSV files can use different encodings, this isn’t the primary reason to choose them over Excel.

CSV files are not vendor-specific (Option B): CSV is a plain-text format that can be opened by any software, unlike Excel files, which are tied to Microsoft Excel, making CSV more interoperable and the best reason for sharing.

CSV files are smaller in size (Option C): CSV files are often smaller due to their simplicity, but this isn’t always the primary reason for sharing.

CSV files are easier to change in text editors (Option D): While true, this isn’t the most compelling reason for sharing data across systems.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and CSV’s vendor-neutral nature makes it ideal for sharing data.

This question pertains to the Visualization and Reporting domain, focusing on survey design considerations, particularly for accessibility. The survey is for Paralympic athletes, who may have visual impairments, requiring specific design considerations.

Idioms (Option A): Idioms (e.g., colloquial phrases) might confuse non-native speakers, but they’re not a primary survey design concern for Paralympic athletes.

Color contrast (Option B): High color contrast ensures readability for athletes with visual impairments (e.g., color blindness), a critical accessibility consideration for Paralympic surveys.

Refresh speed (Option C): Refresh speed is relevant for dashboards, not static surveys.

Granularity (Option D): Granularity refers to data detail levels, not a survey design consideration.

The DA0-002 Visualization and Reporting domain includes "translating business requirements to form the appropriate visualization," and color contrast is a key accessibility factor in survey design for diverse audiences.

This question falls under the Data Analysis domain, focusing on analyzing discrepancies in data reports. The total distinct messages are fewer than the total emails sent, indicating a specific issue.

The aggregation did not include all emails (Option A): If the aggregation missed emails, the total sent would be lower, not the distinct count.

Some emails were not delivered (Option B): Undelivered emails would reduce the total sent, but the scenario implies the total sent is accurate.

The report failed to run properly (Option C): A report failure would likely cause broader issues, not a specific discrepancy between distinct and total counts.

A recipient received duplicate emails (Option D): If recipients received duplicates, the total emails sent would be higher than the distinct messages (unique email content), explaining the difference.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and identifying duplicates is a common analysis task to explain such discrepancies.

This question pertains to the Data Governance domain, focusing on data quality validation techniques. The task is to validate that the number of records matches expectations, which requires a specific type of assessment.

Source control (Option A): Source control (e.g., Git) manages code versions, not dataset validation.

Unit test (Option B): A unit test checks a specific component of a process, such as verifying that the number of records in a dataset matches the expected count, making it the best fit.

Stress test (Option C): Stress tests evaluate system performance under load, not record counts.

Health check (Option D): A health check monitors system status but isn’t specific to validating record counts.

The DA0-002 Data Governance domain includes "data quality control concepts," and unit tests are a standard method for validating specific data outcomes like record counts.

This question falls under the Data Concepts and Environments domain, focusing on database operations like joins. A FULL JOIN combines all rows from both tables, including matches and non-matches, filling in NULLs where there’s no corresponding data.

The first table has IDs: 23525 (Frank), 11290 (Martha), 12141 (Ellen).

The second table has IDs: 23525, 11040, 11290.

A FULL JOIN includes all IDs: 23525, 11290, 12141, 11040.

23525 matches (Frank with 1234 Harding, Chicago, IL).

11290 matches (Martha with 2 Speedway, Miami, FL).

12141 has no match in the second table, so Address, City, and State are NULL.

11040 has no match in the first table, so Name and Date of birth are NULL.

Option A: Incorrect; it includes a row for Ellen with "2 Speedway," but Ellen’s ID (12141) doesn’t match any address, and 11040 is missing.

Option B: Identical to Option A, so incorrect for the same reasons.

Option C: Incorrect; it mismatches addresses (e.g., Ellen with 935 Terrace Hills, which belongs to 11040).

Option D: Correct; it includes all IDs, with NULLs for non-matching rows (Ellen has no address, and 11040 has no name).

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as performing joins in relational databases.

This question is part of the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a given dataset. The grocery store wants to view revenue by department, which requires a chart that shows proportions or comparisons across categories.

Gantt (Option A): Gantt charts are used for project scheduling, not for comparing revenue across categories.

Pie (Option B): Pie charts are ideal for showing proportions or percentages of a whole, such as revenue distribution across departments, making this the best choice.

Area (Option C): Area charts are better for showing trends over time, not static categorical comparisons.

Line (Option D): Line charts are used for trends over time, not for comparing discrete categories like departments.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization" , and a pie chart is the most appropriate for showing departmental revenue proportions.

This question pertains to the Data Acquisition and Preparation domain, focusing on tools for automating repeatable tasks in data processes. The task is to identify the most efficient programming method.

LLM (Option A): Large Language Models (e.g., GPT) are for text generation, not efficient for repeatable data tasks.

Deep learning (Option B): Deep learning is for complex pattern recognition (e.g., image classification), not efficient for simple repeatable tasks.

NLP (Option C): Natural Language Processing is for text analysis, not general repeatable tasks.

RPA (Option D): Robotic Process Automation (RPA) automates repetitive, rule-based tasks (e.g., data entry, file processing), making it the most efficient for programming repeatable tasks.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and RPA is a widely used method for automating repeatable data tasks.

This question pertains to the Visualization and Reporting domain, focusing on dashboard design for specific audiences. Internal technical audiences (e.g., data analysts, IT staff) need actionable, data-driven insights.

Methodology section (Option A): Methodology is important for research reports, not dashboards, especially for technical audiences who prioritize data.

Dynamic features (Option B): Dynamic features (e.g., interactivity) are useful but not the most critical element for technical audiences.

Key performance indicators (Option C): KPIs provide critical metrics (e.g., system uptime, error rates) that technical audiences need to monitor and act on, making this the most important element.

Company branding (Option D): Branding is more relevant for external audiences, not internal technical ones.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and KPIs are essential for technical dashboards.

This question pertains to the Data Concepts and Environments domain, focusing on data sources that facilitate online data access. The task is to identify a source that simplifies online data consumption.

Data mart (Option A): A data mart stores structured data for specific business areas, typically accessed internally, not designed for online consumption.

Web scraping (Option B): Web scraping extracts data from websites but requires parsing and cleaning, which isn’t necessarily "easier."

Database (Option C): Databases store data but aren’t inherently designed for online consumption without an interface.

Application programming interface (Option D): An API provides a structured way to access data online, often in formats like JSON, making data consumption easier for applications and users.

The DA0-002 Data Concepts and Environments domain includes understanding "data sources," and APIs are specifically designed to simplify online data access.

This question pertains to the Data Concepts and Environments domain, focusing on methods for managing software and data environments. The task is to identify a method that combines files, software, and libraries for use across different systems.

Package manager (Option A): Package managers (e.g., npm) manage software dependencies but don’t combine files and libraries for cross-system use.

Code repository (Option B): Code repositories (e.g., GitHub) store code but don’t package it for deployment across environments.

Virtual machine (Option C): Virtual machines emulate entire operating systems, which is heavier than needed for combining files and libraries.

Containerization (Option D): Containerization (e.g., Docker) packages files, software, and libraries into a container that can run consistently across different operating systems and environments, making it the best choice.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and containerization is a standard method for ensuring consistency across systems.