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V1

Data+ V1 (Retiring Version)

CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. Equip yourself with skills to better analyze and interpret data, communicate insights, and demonstrate competency. CompTIA Data+ validates that certified professionals have the skills required to facilitate data-driven business decisions.

The English exam has retired. This exam remains available in Japanese and Thai until July 16, 2026. The latest version, Data+ V2, is now available.

Plus Data+ Certification

Skills learned

  • Identify basic concepts of data schemas and dimensions and understand the differences between common data structures and file formats to build a strong foundation in data concepts and environments.

  • Apply data acquisition, cleansing, profiling, and manipulation techniques to enhance data mining skills.

  • Use appropriate descriptive statistical methods and summarize types of analysis and critical analysis techniques for effective data analysis.

  • Translate business requirements into meaningful visualizations by creating reports or dashboards.

  • Summarize key data governance concepts and apply data quality control techniques to ensure accuracy and compliance.

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Exam details

  • Exam version: V1

  • Exam series code: DA0-001

  • Launch date: February 28, 2022

  • Number of questions: maximum of 90 questions

  • Types of questions: multiple-choice and performance-based

  • Duration: 90 minutes

  • Passing score: 675 (on a scale of 100-900)

  • Languages: English, Japanese, and Thai

  • Recommended experience: 18–24 months in a report or business analyst job role, with exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience

  • Retirement: English – April 14, 2026; Japanese and Thai – July 16, 2026

 

Data+ (V1) exam objectives summary

Data concepts and environments (15%)

  • Data schemas and dimensions: identifying databases, data marts, data warehouses, data lakes, and slowly changing dimensions.
  • Data types: comparing date, numeric, alphanumeric, currency, text, discrete vs. continuous, categorical/dimension, images, audio, and video.
  • Data structures and file formats: comparing structured and unstructured data and file formats like text/flat files, JavaScript object notation (JSON), extensible markup language (XML), and hypertext markup language (HTML).

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Data mining (25%)

  • Data acquisition: explaining integration methods like delta load, extract/load/transform (ELT), and collection methods like web scraping, application programming interfaces (APIs), surveys, sampling, and observation.
  • Data cleansing and profiling: identifying duplicate data, missing values, invalid data, outliers, specification mismatches, and data type validation.
  • Data manipulation techniques: executing techniques like merging, blending, concatenation, appending, imputation, aggregation, transposing, normalizing, and parsing.
  • Query optimization: explaining filtering, sorting, date functions, logical functions, aggregate functions, indexing, temporary tables, and execution plans.

Data analysis (23%)

  • Descriptive statistics: applying measures of central tendency, dispersion, frequencies, percentages, percent change, and confidence intervals.
  • Inferential statistics: explaining t-tests, z-scores, p-values, chi-squared tests, hypothesis testing, regression, and correlation.
  • Analysis techniques: summarizing trend analysis, performance analysis, exploratory analysis, and link analysis.

Visualization (23%)

  • Business requirements: translating requirements into reports using measures of central tendency, dispersion, and percentages.
  • Report and dashboard design: using cover pages, design elements, and documentation.
  • Dashboard development: applying considerations for development processes and delivery.
  • Visualization types: applying line charts, pie charts, scatter plots, bar charts, histograms, heat maps, geographic maps, tree maps, stacked charts, and word clouds.
  • Report types: comparing static vs. dynamic, ad-hoc, self-service, recurring, and tactical research reports.


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Data governance, quality, and controls (14%)

  • Data governance: summarizing access, security, storage, use, entity relationships, classification, jurisdiction, and breach reporting.
  • Data quality control: applying validation methods, quality dimensions, rules, metrics, and automated checks.
  • Master data management (MDM): explaining processes and circumstances for MDM.