CompTIA Data+ DA0-002 (V2) Exam Objectives
This page translates the CompTIA Data+ DA0-002 (V2) Exam Objectives into clear, practical language. You’ll see what each domain covers, the kinds of tasks that show up on the exam, and a focused study plan that turns objectives into repeatable labs, PBQs, and job-ready skills. The current blueprint includes 5 domains: Data Concepts & Environments, Data Acquisition & Preparation, Data Analysis, Visualization & Reporting, and Data Governance.
Last updated: November 9, 2025. Always verify against the official CompTIA blueprint before your exam.
TL;DR: Data+ validates entry-to-mid data skills: translate business needs, acquire and prepare data, analyze with basic statistics, create clear visuals and reports, and follow governance and quality standards across the data life cycle. Expect up to 90 questions in 90 minutes, with multiple-choice and performance-based items.
Table of Contents
- What Is Covered on CompTIA Data+ DA0-002 (V2)?
- Objective-By-Objective Breakdown (with examples)
- Why Data+ for Analyst/BI Roles?
- Study Plan: How to Master the DA0-002 Objectives
- Free Checklist & Next Steps
- Frequently Asked Questions
What Is Covered on CompTIA Data+ DA0-002 (V2)?
Data+ validates day-to-day data analyst capabilities across on-prem, cloud, and hybrid stacks. You’ll be tested on how to:
- Translate business requirements into data work (acquisition, prep, transformation)
- Use common tools (SQL, BI platforms, notebooks, libraries) to analyze and visualize data
- Apply statistical reasoning to guide decisions and communicate outcomes
- Respect governance, privacy, quality, and compliance throughout the data life cycle
Objective-By-Objective Breakdown (with examples)
1) Data Concepts and Environments — 20%
Anchor core data knowledge and the ecosystem you’ll work in.
- Concepts: database types (relational vs. non-relational), file types (.csv, .xlsx, .json), structured/semi/unstructured data, data types (string, numeric, boolean, datetime, BLOB/CLOB, GUID/UUID)
- Sources & repos: databases, APIs, websites, files, logs, data lakes/lakehouses/warehouses/marts/silos
- Infrastructure: cloud providers, deployment models (public/private/hybrid), storage types (object/file/block; local/shared), containerization basics
- Tools: IDEs/notebooks, BI tools (Tableau/Power BI/Looker), libraries (Anaconda, pandas, tidyverse), DBMS tools (SQL, SSMS, MySQL Workbench, MongoDB Compass, DBeaver)
- AI awareness: generative AI/LLMs, foundational models, deep learning, NLP, RPA/automated reporting
2) Data Acquisition and Preparation — 22%
Get, explore, and clean data so it can be trusted.
- Acquisition: integration; SQL querying (joins, unions, filters, grouping, aggregates, nesting); basic optimization (indexes, params, subsets, temp tables); ETL vs. ELT; data collection (surveys, sampling)
- Exploration: missing values, duplication/redundancy, outliers, completeness, validation
- Transformation & cleansing: string manipulation/RegEx, conversions, binning, augmentation, explode, scaling/standardization, imputation, parsing, merge/append, derived fields, deletion
3) Data Analysis — 24%
Use methods, functions, and troubleshooting to produce insight.
- Communication approach: mock-ups, accessibility, technical vs. non-technical, level of detail, internal vs. external, personas (C-suite vs. IC), sensitivity, KPIs
- Methods: descriptive/predictive/prescriptive/inferential basics; functions (math—central tendency & dispersion; logical; date; string)
- Troubleshooting: connectivity issues, user-reported problems, basic SQL errors, corrupted data; enable logging, validate sources, consult vendor/community resources
4) Visualization and Reporting — 20%
Present data so decisions are faster and clearer.
- Visual elements: charts, maps, pivot tables, infographics; design elements—labels, legends, branding, color schemes
- Delivery/consumption: executive summaries, self-service portals, dashboards (static/dynamic), cadence (recurring vs. ad hoc), versioning (snapshot vs. real-time)
- Report validation & troubleshooting: excessive load/refresh time, large data size, misbehaving filters, stale/corrupt data; use filtering, code/calc/peer review, source validation, watch for data structure change and monitoring alerts
5) Data Governance — 14%
Present data so decisions are faster and clearer.
- Protect integrity, privacy, and quality at scale.
- Management concepts: integration, documentation, flow diagrams, explainability reports, data dictionary, hierarchy/lineage, source of truth, versioning (snapshots/refresh intervals), metadata
- Compliance: retention, GDPR & jurisdiction, storage, data ethics; PCI DSS, audit, classification, incident reporting (breach/security)
- Privacy & protection: RBAC, encryption (in transit/at rest), data usage/sharing, PII/PHI, anonymization/masking, NIST awareness
- Quality assurance: requirement testing, stress/UAT, source control, unit tests, health checks & drifts, automated DQ monitoring, profiling & metrics, ISO awareness
Why Data+ for Analyst/BI Roles?
Data+ proves you can move from a question to a dataset to a decision—ethically and reproducibly. It aligns with junior data analyst, BI analyst, reporting analyst, and operations analyst roles where SQL, BI tools, basic statistics, and governance hygiene matter.
Study Plan: How to Master the DA0-002 Objectives
- Map each objective to a mini-lab
Import a CSV, profile it, clean it (nulls/outliers), and load it to a warehouse/lakehouse. Save steps as a reproducible notebook/SQL script. - Build a small BI project
Create a KPI dashboard with snapshot vs. real-time views. Document data lineage, refresh intervals, and accessibility choices. - Practice “stats you actually use”
Compute mean/median/std, percent change, z-scores, simple correlations. Explain which metric you’d present to a non-technical stakeholder and why. - Triage and validate
Break a report: change a field name, slow the source, or alter a filter. Troubleshoot and log root cause + fix. Add a validation checklist. - Govern as you go
Add metadata, a data dictionary entry, retention notes, and a mini explainability note for any derived KPI.
