Free Practice Questions for CompTIA Data+ Certification

    ๐Ÿ”„ Last checked for updates July 5th, 2026

    Study with 345 exam-style practice questions designed to help you prepare for the CompTIA Data+.

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    Exam Details

    Key information about CompTIA Data+

    Official study guide

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    Question formats CertSafari offers
    • Multiple choice
    • Matching
    duration:

    90 minutes

    languages:

    English

    retirement:

    Usually three years after launch (estimated 2028)

    launch date:

    October 14, 2025

    exam version:

    V2

    passing score:

    675 (on a scale of 100โ€“900)

    exam series code:

    DA0-002

    number of questions:

    Maximum of 90 (multiple-choice and performance-based)

    recommended experience:

    18โ€“24 months in a data analyst or similar job role, with exposure to databases, analytical tools, basic statistics, and data visualization

    accreditation and benefits:

    ISO accredited by the ANSI National Accreditation Board (ANAB), and mapped to the NICE Framework Data Analyst (IO-WRL-001) work role.

    Exam Topics & Skills Assessed

    Skills measured (from the official study guide)

    Domain 1: Data concepts and environments

    Subdomain 1.1: Explain data concepts: Database types, data structures, file extensions, and data types.

    Explain data concepts: Database types, data structures, file extensions, and data types.

    Subdomain 1.2: Identify data sources: Databases, APIs, website data, files, logs and repositories.

    Identify data sources: Databases, APIs, website data, files, logs and repositories.

    Subdomain 1.3: Recognize infrastructure concepts: Cloud, on-premise, storage, and containerization.

    Recognize infrastructure concepts: Cloud, on-premise, storage, and containerization.

    Subdomain 1.4: Identify data tools: Coding environments, BI software, and analysis platforms.

    Identify data tools: Coding environments, BI software, and analysis platforms.

    Subdomain 1.5: Understand AI concepts: Identify AI models, natural language processing, and robotic automation.

    Understand AI concepts: Identify AI models, natural language processing, and robotic automation.

    Domain 2: Data acquisition and preparation

    Subdomain 2.1: Use data acquisition methods: Data integration and queries to gather and combine data.

    Use data acquisition methods: Data integration and queries to gather and combine data.

    Subdomain 2.2: Perform data exploration: Find missing values, duplication, redundancy, or outliers.

    Perform data exploration: Find missing values, duplication, redundancy, or outliers.

    Subdomain 2.3: Apply data transformation: Cleansing, merging, parsing, and formatting data.

    Apply data transformation: Cleansing, merging, parsing, and formatting data.

    Domain 3: Data analysis

    Subdomain 3.1: Communicate analysis results: Select methods for different audiences.

    Communicate analysis results: Select methods for different audiences.

    Subdomain 3.2: Select statistical methods: Apply basic statistical techniques to data.

    Select statistical methods: Apply basic statistical techniques to data.

    Subdomain 3.3: Troubleshoot analysis issues: Use tools and resources to resolve problems.

    Troubleshoot analysis issues: Use tools and resources to resolve problems.

    Domain 4: Visualization and reporting

    Subdomain 4.1: Create effective visuals: Use charts, maps, tables, and design elements.

    Create effective visuals: Use charts, maps, tables, and design elements.

    Subdomain 4.2: Deliver reports: Provide dashboards or summaries using appropriate methods.

    Deliver reports: Provide dashboards or summaries using appropriate methods.

    Subdomain 4.3: Validate reporting accuracy: Apply validation and review to solve reporting issues

    Validate reporting accuracy: Apply validation and review to solve reporting issues

    Domain 5: Data governance

    Subdomain 5.1: Explain data management practices: Documentation, versioning, and data lineage.

    Explain data management practices: Documentation, versioning, and data lineage.

    Subdomain 5.2: Summarize compliance requirements: Retention, audits, and regulations.

    Summarize compliance requirements: Retention, audits, and regulations.

    Subdomain 5.3: Compare privacy and protection strategies: Access control, encryption, and masking.

    Compare privacy and protection strategies: Access control, encryption, and masking.

    Subdomain 5.4: Implement quality assurance: Profiling, monitoring, and testing for data quality.

    Implement quality assurance: Profiling, monitoring, and testing for data quality.

    Techniques & products

    Database types
    data structures
    file extensions
    data types
    Databases
    APIs
    website data
    files
    logs
    repositories
    Cloud
    on-premise
    storage
    containerization
    Coding environments
    BI software
    analysis platforms
    AI models
    natural language processing
    robotic automation
    Data integration
    queries
    missing values
    duplication
    redundancy
    outliers
    Cleansing
    merging
    parsing
    formatting data
    statistical methods
    charts
    maps
    tables
    design elements
    dashboards
    summaries
    validation
    review
    Documentation
    versioning
    data lineage
    Retention
    audits
    regulations
    Access control
    encryption
    masking
    Profiling
    monitoring
    testing for data quality

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