CompTIA · 2026 Edition
A complete preparation guide written by CompTIA-certified engineers. Covers the exam format,all 5 blueprint domains, a week-by-week study plan, and proven tips for passing first time.
3–5 months
Prep time
Intermediate
Difficulty
90
Exam questions
675/1000
Pass mark
Exam code
DA0-001
Full name
CompTIA Data+
Vendor
CompTIA
Duration
90 minutes
Questions
90 items
Passing score
675/1000 (scaled)
Domains covered
5 blueprint domains
Recommended experience
Basic familiarity with data concepts; no formal prerequisites, but spreadsheet or database experience is helpful
Typical prep time
3–5 months
Data+ validates foundational data analyst skills — data mining, visualisation, analysis, and governance. It is a strong complement to certifications like Power BI or Tableau and is used as a hiring filter for junior analyst roles.
Job roles this opens
Domain percentage weights are not currently available for this exam. The checklist below is still useful for planning your study.
Weeks 1–3
Data Concepts and Environments: databases, data types, data models
Tip: Know the difference between structured (relational tables), semi-structured (JSON, XML), and unstructured (documents, images, audio) data. Questions describe a data type and ask which storage system or processing approach fits.
Weeks 4–6
Mining Data: query languages, data manipulation, statistical methods
Tip: Basic SQL is tested: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, and JOIN types (INNER, LEFT, RIGHT). You do not need to write complex queries but must interpret a query and predict its output or identify a syntax error.
Weeks 7–9
Analyzing and Visualising Data: statistics, charts, dashboards, storytelling with data
Tip: Know which chart type fits which data story: bar charts for comparisons, line charts for trends over time, scatter plots for correlations, pie charts for part-to-whole (and when NOT to use them). Questions give a scenario and ask which visualisation is most appropriate.
Weeks 10–13
Data Governance, Quality and Controls: data lifecycle, privacy regulations, quality dimensions
Tip: Data governance vocabulary is heavily tested: data lineage, data dictionary, data catalog, master data management, and PII handling. Know what GDPR, HIPAA, and CCPA require at a conceptual level — the exam does not require legal expertise but does test what each regulation protects.
Statistical concepts on Data+ are introductory but include mean, median, mode, standard deviation, normal distribution, and correlation vs causation. Know what each measures and when it is the appropriate summary statistic.
Data types matter for analysis: nominal (categories with no order), ordinal (categories with order), interval (numeric with equal spacing, no true zero), ratio (numeric with true zero). A question describing salary data wants 'ratio'; a question about survey ratings wants 'ordinal'.
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both tested. Know that ELT is preferred in modern cloud data warehouses where the target system can handle transformation at scale.
Data quality dimensions appear throughout the exam: accuracy, completeness, consistency, timeliness, uniqueness, and validity. Know the definition of each and what an organisation would measure to assess it.
Visualisation tools are mentioned by category on Data+ — Tableau, Power BI, and Excel are not specifically named, but questions describe their capabilities. Know what a BI dashboard is, what a pivot table does, and what a heat map communicates.
Apply everything in this guide with adaptive practice questions, detailed answer explanations, and domain analytics.
Deep-dive explanations of the key topics tested on DA0-001 — with exam key points and common misconceptions.