CompTIA Data+ (DA0-002) Practice Exams 2026

About the CompTIA Data+ DA0-002 Exam

CompTIA Data+ is an entry-to-intermediate level certification validating the data analysis skills required for data-related IT roles. Data+ covers the complete data analytics workflow—collecting and preparing data, analyzing it for patterns and insights, visualizing findings for stakeholders, and ensuring data quality and governance. The certification is vendor-neutral, meaning skills apply regardless of whether you use Excel, SQL, Python, Power BI, Tableau, or any other analytics tool. Data+ targets professionals in IT who need to work with data: data analysts, business analysts, data reporting specialists, and IT professionals supporting data infrastructure.

The DA0-002 exam consists of a maximum of 90 questions to complete in 90 minutes, with a passing score of 675 on a 100-900 scale. The exam costs $246 USD—one of CompTIA's most affordable certifications—and is delivered at Pearson VUE testing centers or via online proctored exam. Questions are primarily multiple-choice (single and multiple answer), covering data concepts, analysis techniques, visualization principles, and governance frameworks. Data+ is valid for 3 years, renewable with 20 CEUs, and serves as a foundation for more advanced data certifications (cloud provider data certifications, Google Data Analytics, etc.) or as a complement to IT certifications for professionals in data-adjacent roles.

Data+ DA0-002 Domains and Weighting:

• Domain 1: Data Concepts and Environments (15%) - Data types (structured, unstructured, semi-structured), data storage formats (relational databases, NoSQL, data lakes, data warehouses), database concepts (schemas, normalization, OLTP vs OLAP), data sources and collection methods, metadata management, and common data file formats (CSV, JSON, XML, Parquet, Avro)
• Domain 2: Data Mining (25%) - Data manipulation techniques (filtering, sorting, aggregating, joining datasets), ETL (Extract, Transform, Load) processes, data cleaning and transformation (handling missing values, outliers, duplicates), SQL query basics (SELECT, WHERE, GROUP BY, JOIN, aggregate functions), data sampling methods, and working with APIs and web data sources
• Domain 3: Data Analysis (23%) - Descriptive statistics (mean, median, mode, variance, standard deviation), analytical techniques (trend analysis, correlation, regression basics, cohort analysis), statistical testing concepts (hypothesis testing, p-values, confidence intervals), time series analysis, segmentation and clustering concepts, and interpreting analytical results for business decisions
• Domain 4: Visualization and Reporting (22%) - Chart and graph types and their appropriate use cases (bar charts for comparisons, line charts for trends, scatter plots for correlation, heat maps for density, pie charts for composition), dashboard design principles (clarity, actionability, appropriate audience), report creation for executive and technical audiences, data storytelling, and BI tool concepts (Power BI, Tableau, Google Looker Studio)
• Domain 5: Data Governance, Quality, and Controls (15%) - Data quality dimensions (accuracy, completeness, consistency, timeliness, validity), data governance frameworks, data privacy regulations (GDPR, CCPA, HIPAA data handling requirements), data lifecycle management, master data management, data lineage, and regulatory compliance requirements for data handling

Data+ is positioned as a foundational data certification accessible to IT professionals without specialized data science backgrounds. It validates the ability to work with data analytically—understanding data structures, performing basic analysis, creating meaningful visualizations, and ensuring data quality—skills increasingly required across IT roles as organizations become more data-driven.

Why Take CompTIA Data+?

• Data Skills Are Now Required Across IT Roles: Data analysis is no longer confined to dedicated data analyst roles. System administrators analyze performance metrics and log data. Security analysts analyze threat data and vulnerability trends. IT managers analyze cost data and resource utilization. Project managers analyze timeline and budget data. Data+ validates the data literacy skills that have become baseline requirements for modern IT professionals—enabling you to work with data confidently regardless of your primary IT role.
• Vendor-Neutral Data Skills Applicable Across All Tools: Data+ validates data analysis concepts rather than specific tool proficiency—skills that apply whether you use SQL databases or NoSQL, Excel or Python, Power BI or Tableau, or any other combination. This vendor neutrality provides flexibility as organizations change their data tool stacks. Unlike Google Data Analytics, Microsoft Power BI, or Tableau certifications that validate single-platform skills, Data+ certifies the analytical thinking and data literacy that make you effective across any data environment.
• Entry Point into High-Growth Data Analytics Career Path: Data analysts are among the fastest-growing IT job categories, with the US Bureau of Labor Statistics projecting 35% growth over the next decade. Entry-level data analysts with Data+ earn $50,000-$70,000, while experienced analysts earn $80,000-$110,000. Data+ provides the credential that helps IT professionals transition into data analyst roles or demonstrates data competency to employers when applying for data-adjacent positions. The certification is particularly valuable for IT professionals who work with BI reporting, database administration, or IT operations analytics.
• Data Governance Coverage Meets Regulatory Demand: With GDPR, CCPA, HIPAA, and other data privacy regulations imposing significant penalties for data mishandling, organizations increasingly need IT professionals who understand data governance, data quality management, and compliance requirements. Data+'s governance domain covers these regulatory requirements at a practical level—understanding data classification, retention policies, privacy by design, and audit requirements. This compliance knowledge adds value for IT professionals in regulated industries (healthcare, finance, government) where data handling errors carry legal consequences.

What You'll Learn in the Data+ DA0-002 Exam

Data+ covers the complete data analytics workflow from understanding data sources to presenting insights to stakeholders. The exam tests both conceptual knowledge (understanding what different analysis techniques accomplish) and practical application (selecting the right technique for a given business question, choosing appropriate visualization types, identifying data quality issues). Unlike programming-focused data certifications, Data+ emphasizes analytical thinking and data literacy over specific coding skills.

Data Concepts and Collection

• Data Types and Storage: Differentiating structured data (relational databases with defined schemas: SQL Server, PostgreSQL, MySQL), semi-structured data (JSON, XML, CSV with variable structure), and unstructured data (text documents, images, audio, video requiring special processing); understanding data storage technologies (data warehouses for historical analysis: Snowflake, Redshift, BigQuery; data lakes for raw unprocessed data: S3, ADLS, GCS; NoSQL databases for flexible schemas: MongoDB, DynamoDB, Cassandra); and understanding OLTP (transactional systems) versus OLAP (analytical systems) use cases
• Data Collection and Sourcing: Understanding primary data collection (surveys, experiments, direct measurement) versus secondary data (existing databases, third-party data, public datasets); working with different data source types (relational databases via SQL, APIs via JSON/XML responses, flat files via CSV/Excel, streaming data); understanding ETL processes and data pipelines; and evaluating data source quality and reliability for analytical purposes
• Metadata and Data Catalogs: Understanding metadata types (descriptive, structural, administrative); using data catalogs for data discovery; documenting data lineage (origin, transformations applied, current state); and understanding master data management (maintaining consistent, accurate reference data across systems)

Data Analysis and Mining

• Data Manipulation and Cleaning: Applying data cleaning techniques: handling missing values (deletion, imputation with mean/median/mode, flagging); identifying and treating outliers (z-score method, IQR method, domain knowledge); removing duplicates and standardizing inconsistent values; normalizing and scaling data for analysis; and transforming data types (converting strings to dates, categorizing continuous variables, encoding categorical variables)
• Statistical Analysis: Calculating and interpreting descriptive statistics (mean, median, mode, range, variance, standard deviation, interquartile range, percentiles); understanding distributions (normal distribution, skewness, kurtosis); calculating and interpreting correlation coefficients; understanding regression basics (predicting continuous outcomes); performing segmentation and cohort analysis; and interpreting p-values and confidence intervals for statistical significance
• SQL for Data Analysis: Writing SQL queries to extract and analyze data (SELECT with WHERE clauses, GROUP BY for aggregation, ORDER BY for sorting, JOIN types for combining tables—INNER, LEFT, RIGHT, FULL); using aggregate functions (COUNT, SUM, AVG, MIN, MAX); writing subqueries and CTEs (Common Table Expressions) for complex analysis; and understanding window functions (ROW_NUMBER, RANK, LAG, LEAD) for time series and ranking analysis

Visualization, Reporting, and Governance

• Data Visualization Best Practices: Selecting appropriate chart types for different data and analytical questions: bar/column charts for comparing categories, line charts for trends over time, scatter plots for correlation between variables, pie/donut charts for part-to-whole relationships (use sparingly), heat maps for showing intensity across two dimensions, box plots for distribution comparison; applying visualization design principles (clear titles and labels, appropriate scales, accessible color choices, avoiding chart junk); and understanding when tables are more appropriate than charts
• Dashboard Design and Reporting: Designing effective dashboards with appropriate KPIs and metrics for the target audience (executive dashboards vs. operational dashboards); creating reports that tell a clear data story with context, findings, and recommendations; understanding drill-down and interactive features in BI tools; presenting findings appropriately for technical and non-technical audiences; and creating automated reports for recurring analysis needs
• Data Governance and Quality: Implementing data quality checks (accuracy validation, completeness assessment, consistency checks, timeliness monitoring); understanding data governance frameworks and their organizational benefits; applying data privacy principles (data minimization, purpose limitation, subject rights for GDPR/CCPA compliance); managing data retention and disposal policies; and documenting data quality issues and resolutions for audit purposes

How to Prepare for the Data+ DA0-002 Exam

Data+ preparation typically takes 6-10 weeks for candidates with basic IT background and some exposure to data concepts, or 10-14 weeks for those entirely new to data analysis. Unlike technically deep certifications (Linux+, CySA+), Data+ focuses on data literacy and analytical thinking accessible to non-programmers. You don't need to write complex code to pass Data+, but you should have hands-on experience with basic data manipulation, statistical concepts, and visualization tools. Combining structured study with practical data projects produces the best results.

1. Build Data Foundations with Structured Study Materials (3-5 weeks): Begin with the CompTIA Data+ Study Guide covering all five domains. Pay special attention to data types, storage concepts, and the ETL process—these foundational concepts underpin all subsequent domains. Study statistics concepts carefully: many IT professionals find descriptive statistics (mean, median, standard deviation) and statistical testing (hypothesis testing, p-values) unfamiliar. Create a reference sheet for statistical concepts with plain-language explanations and formulas. Mike Chapple's Data+ study resources provide clear explanations of both technical and statistical content. Supplement with free Google or LinkedIn Learning data analysis courses for additional context.
2. Practice SQL and Data Manipulation Hands-On (2-3 weeks): SQL is the most widely used data tool and appears throughout Data+ questions on data mining and analysis. Practice SQL with Mode Analytics SQL Tutorial (free, browser-based), SQLZoo (free, interactive), or Khan Academy SQL course. Focus on: SELECT queries with WHERE filtering, GROUP BY aggregations (COUNT, SUM, AVG), JOIN types (understand when to use INNER vs LEFT JOIN), and subqueries. Practice with real datasets—Kaggle provides thousands of free datasets to analyze. Also experiment with Excel or Google Sheets for data manipulation, pivot tables for aggregation, and basic charting—these tools demonstrate data analysis concepts without requiring programming knowledge.
3. Practice Data Visualization with BI Tools (2-3 weeks): Visualization and Reporting (22% of exam) requires understanding which chart type is appropriate for different data and questions. Download Tableau Public (free) or Power BI Desktop (free) and practice creating different chart types with sample datasets. For each chart you create, think about: what business question does this answer, is this the most appropriate chart type, does the title and labeling clearly communicate the insight? Practice creating a simple dashboard combining multiple charts. This hands-on experience makes visualization questions intuitive rather than theoretical.
4. Study Data Governance and Complete Practice Questions (2 weeks): The governance domain (15%) covers data quality, privacy regulations, and compliance—content that reads quickly but requires understanding regulatory requirements. Study GDPR and CCPA at a high level: what data rights they grant consumers, what obligations they create for organizations, and what constitutes a data breach requiring notification. Study data quality dimensions (accuracy, completeness, consistency, timeliness, validity) and how to assess each. Complete 300+ practice questions from CompTIA's official practice tests or Jason Dion's Data+ resources. For each incorrect answer, review the underlying concept rather than just noting the correct answer. Review the official CompTIA Data+ exam page for current objectives.

Data+ study tip: Create a personal data project throughout your preparation—download a public dataset (Kaggle, data.gov, World Bank Open Data), clean it, analyze it, and create a simple dashboard. This practical experience reinforces concepts across all five domains simultaneously and gives you concrete examples to reference when answering scenario-based exam questions. Budget 150-200 study hours for candidates with some data or IT background, 200-250 hours for those starting fresh.

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