Data+ DA0-002 Exam Objectives: All Five Domains Broken Down

The efficient way to prepare is to follow the weighting rather than your comfort zone. CompTIA Data+ DA0-002 tells you exactly where the points are, and this article walks through all five domains, what each covers in practice, and how to prioritize your time.

For exam mechanics, see the format guide. For the whole path, the complete Data+ guide ties it together.

The weighting at a glance

The weighting is unusually even — no domain dominates. But Data Analysis (24%) and Data Acquisition and Preparation (22%) together are nearly half the exam, so statistics and data clean-up carry the most weight. Governance is the smallest slice, and it's the easiest place to lose points if you skip it, because the questions are policy-driven rather than technical.

Domain 1: Data Concepts and Environments (20%)

The foundation — what data is and where it lives.

Expect database types (relational vs. non-relational), file types (.csv, .xlsx, .json, XML), structured, semi-structured, and unstructured data, and data types (string, numeric, boolean, datetime, BLOB/CLOB, GUID/UUID). You'll also cover data sources and repositories: databases, APIs, websites, files, logs, and the distinctions between data lakes, lakehouses, warehouses, marts, and silos.

This is where V2's new infrastructure content sits: public, private, and hybrid cloud models, object/file/block storage, and containerization basics. AI awareness also appears here — generative AI, LLMs, foundational models, and NLP at a conceptual level, reflecting how AI tooling is reshaping analyst work. You don't need to build models; you need to know what these things are and where they fit.

Domain 2: Data Acquisition and Preparation (22%)

Getting data and making it usable — the unglamorous work that dominates real analyst jobs.

It covers integration and collection methods (APIs, web scraping, surveys, sampling), the ETL vs ELT distinction (V2 explicitly tests when each is appropriate), and data cleansing and profiling: duplicates, missing values, invalid data, outliers, specification mismatches, and type validation. Manipulation techniques include merging, blending, concatenation, appending, imputation, aggregation, transposing, normalizing, and parsing. Query optimization rounds it out — filtering, sorting, date and logical functions, aggregate functions, indexing, temporary tables, and execution plans.

This domain is where SQL fluency pays off most. If SQL is new to you, start here and start early.

Domain 3: Data Analysis (24%)

The largest domain — the actual analysis.

You'll apply descriptive statistics (mean, median, mode, standard deviation, percent change, z-scores), identify trends, patterns, outliers, and relationships, work with correlation, and summarize types of analysis and critical analysis techniques. The skill tested is judgment as much as calculation: knowing which statistical method fits a business question, and which metric you'd actually present to a non-technical stakeholder and why.

Practice the statistics you'd genuinely use, not exotic theory. And be ready to reason from a scenario to the right analytical approach.

Domain 4: Visualization and Reporting (20%)

Turning analysis into something people can act on.

It covers visual elements (charts, maps, pivot tables, infographics) and design elements (labels, legends, colour schemes, branding); choosing the right visual for the message and audience; and delivery and consumption — executive summaries, self-service portals, static vs. dynamic dashboards, recurring vs. ad hoc cadence, and snapshot vs. real-time versioning. Report validation and troubleshooting is tested too: excessive load or refresh times, oversized data, misbehaving filters, stale or corrupt data, and the fixes — filtering, code and peer review, source validation, and monitoring alerts.

V2 references modern tools specifically — Power BI, Tableau, Looker — so familiarity with a BI platform helps.

Domain 5: Data Governance (14%)

The smallest domain, but don't skip it — it's policy-driven, learnable, and a reliable source of points.

It covers management concepts (integration, documentation, flow diagrams, explainability reports, data dictionaries, hierarchy and lineage, source of truth, versioning, metadata); compliance (retention, GDPR and jurisdiction, storage, data ethics, PCI DSS, audit, classification, breach and incident reporting); and privacy and protection (RBAC, encryption in transit and at rest). V2 strengthened this coverage — governance matters more, not less, as AI enters the workflow.

How to sequence your study

A sensible order: start with Data Concepts to build vocabulary, then invest heavily in Data Acquisition and Preparation (and SQL) since it's weighty and foundational, then Data Analysis as the largest domain, then Visualization and Reporting with hands-on BI practice, and finish with Governance — concrete, learnable points. The study plan lays this out week by week.

Because Data+ rewards working with real data, hands-on practice mapped to the objectives is the most efficient path.

Practice against the objectives: CompTIA Data+ CertMaster Labs (DA0-002) put you in browser-based environments where you run queries, clean datasets, and build visualizations. To cover every objective in a structured course, pair them with CertMaster Learn (DA0-002). As an Authorized CompTIA Partner, these are the official versions.

FAQ

Which domain is most important? Data Analysis at 24%, closely followed by Data Acquisition and Preparation at 22%. Together they're nearly half the exam.

How much SQL do I need? Enough to be fluent with joins, GROUP BY, aggregate functions, subqueries, filtering, and basic optimization. SQL is the biggest hurdle for newcomers.

Do I need to know AI for DA0-002? At an awareness level — generative AI, LLMs, foundational models, and NLP concepts appear in Data Concepts. You don't need to build models.

Which BI tool should I learn? Any mainstream one. V2 references Power BI, Tableau, and Looker specifically, but the tested skills are about choosing and validating visuals, not one vendor's menus.

Is Governance worth studying at only 14%? Yes. It's the easiest place to lose points if skipped, and the questions are policy-driven and very learnable.