CompTIA Data+ (DA0-002) Study Guide: All Five Domains, Where the Points Are
Last updated October 24, 2026
Data+ is CompTIA's vendor-neutral entry point into data analytics — built for people who work with data as part of a broader role (business analyst, junior data analyst, report builder) rather than specialized data engineers or scientists. DA0-002 (Version 2.0) spans the full lifecycle of a data task: understanding what you're working with, getting it ready, analyzing it, presenting it, and governing it responsibly. Here's how the exam is weighted, what each domain actually tests, and where candidates lose points.
How the exam is weighted
CompTIA publishes an official domain breakdown for DA0-002 (Version 2.0), and it should directly drive how you allocate study time:
- 1.0 Data Concepts and Environments (20%) — data types (structured, semi-structured, unstructured), data structures, and the environments and systems data lives in.
- 2.0 Data Acquisition and Preparation (22%) — gathering data from multiple sources, integrating it, profiling it for quality issues, and cleaning and transforming it before analysis.
- 3.0 Data Analysis (24%) — the largest domain. Descriptive statistics, identifying patterns and relationships, and tailoring how findings get communicated to different audiences.
- 4.0 Visualization and Reporting (20%) — choosing the right chart for the data and the question being asked, and building dashboards and reports that communicate clearly rather than just decoratively.
- 5.0 Data Governance (14%) — data lineage, versioning, retention, quality standards, and access control: the practices that keep data trustworthy and compliant over time.
Data Analysis carries the most weight at 24%, with Data Acquisition and Preparation close behind at 22% — together, over nearly half the exam tests what you do with data once you have it and before you present it. Data Governance is the lightest domain at 14%, but it's still worth real points and tends to get the least attention from candidates who assume "governance" means policy trivia rather than testable, scenario-based content.
What each domain actually tests
Data Concepts and Environments tests whether you can correctly classify data by type and pick the right storage or processing approach for it — JSON with flexible fields is semi-structured, a normalized relational table is structured, a scanned document is unstructured, and each one calls for a different handling strategy. The exam likes scenarios that mix several data types in one question and ask which combined approach fits all of them.
Data Acquisition and Preparation covers the unglamorous but heavily tested work of getting data ready: combining sources through mapped identifiers (data integration), profiling a dataset to spot blank required fields or inconsistent formats (data quality issues), and cleaning or transforming values before anyone analyzes them. A recurring exam pattern is naming a specific step in a workflow and asking which stage of acquisition or preparation it belongs to.
Data Analysis blends descriptive statistics (mean, median, mode, and when each is the right summary — the median over the mean for a skewed distribution with outliers, for instance) with communication judgment: the exam frequently describes two different audiences for the same findings, one wanting a concise business summary and the other wanting full methodological detail, and asks which communication style fits which audience.
Visualization and Reporting tests chart selection against the question being asked — a line chart for a trend over time, a bar chart for comparing categories, a scatter plot for the relationship between two numeric variables — plus dashboard design principles like consistent color use, since reusing the same color for opposite meanings (profit in one chart, loss in another) is a classic exam trap.
Data Governance covers the vocabulary that keeps data trustworthy over time: data lineage (tracking where data came from and how it changed), versioning, retention policies tied to legal requirements, and access control. These concepts show up as short scenario questions naming a practice and asking which governance term it represents.
How long should you study
Candidates already working with spreadsheets, basic queries, or BI dashboards in their current role typically prepare in four to six weeks, concentrated on the statistics terminology and governance vocabulary that's less familiar from day-to-day tool use. Candidates newer to data work altogether, even with strong general IT or business backgrounds, often need seven to nine weeks to build comfortable familiarity across all five domains, particularly the acquisition/preparation and governance content that doesn't overlap much with general office or IT experience.
Where candidates lose points
- Treating Data Governance as policy trivia instead of applied vocabulary. At 14% it's the lightest domain, but lineage, versioning, retention, and access-control questions are scenario-based, not definitional, and reward candidates who can recognize the concept in a described situation.
- Picking the mean by default. The exam deliberately tests when the median is the better summary statistic — skewed data with outliers is the signal to watch for.
- Choosing a visually appealing chart over the correct one. Pie charts, infographics, and decorative visuals show up as tempting but wrong answers when the data actually calls for a line or bar chart.
- Missing which acquisition/preparation stage a described activity belongs to. Data integration, profiling, and transformation are tested as distinct stages, and confusing them costs points on otherwise straightforward questions.
Why full-rationale practice questions help
Data+ leans heavily on scenario-based recognition — naming a practice, a chart type, or a statistic and asking which concept it represents — so the value of practice is in understanding why the three wrong options describe a different concept entirely, not just picking the right label. Full rationale on every choice makes those boundaries clear: once you can explain why a pivot table isn't the right answer to a correlation question, or why access control isn't the same as data lineage, those distinctions stop being guesswork on exam day.
CompTIA Data+ (DA0-002 V2) - 700 Questions & Full Rationale Answers — $19.99, instant PDF download with full rationale on every answer.
View the practice questions →If you're building a broader IT or analytics path
Data+ pairs naturally with a project-management foundation for candidates who coordinate data initiatives, or with a security-analyst track for candidates whose data work touches monitoring and detection: