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Exam Information
Official specifications published by CompTIA
Exam Format
90 min
Up to 90
720
Entry-to-Mid Level
Registration
$255 USD
Pearson VUE or online proctoring
English
Validity
3 years
Complete CompTIA CertMaster CE for Data+; Earn 20 Continuing Education Units (CEUs) within the 3-year cycle; Pass a higher-level CompTIA certification
DA0-002 Exam Topics and Domains
DA0-002 is organized into 5 weighted domains. Expect to work with Anaconda, AWS, Azure, DBeaver, and more.
1
Data Concepts and Environments
20%
Explain data concepts
Database typesData structuresFile extensions and formatsData types
- Explain fundamental data concepts including database types, data structures, file formats, and data types
- Differentiate between relational and non-relational databases
- Classify data as structured, semi-structured, or unstructured
- Identify appropriate file formats and data types for given scenarios
Identify types of data sources
Data sourcesData repositories
- Identify common types of data sources including databases, APIs, websites, files, and logs
- Distinguish between different data repositories such as data lakes, warehouses, marts, and lakehouses
- Understand the differences between OLTP and OLAP systems
Identify infrastructure concepts
Cloud providersDeployment modelsStorage typesContainerization
- Identify major cloud providers and their relevance to data analytics
- Understand deployment models including public, private, and hybrid cloud
- Differentiate storage types including object, file, and block storage
- Recognize basic containerization concepts
Identify common data analysis tools
Coding environmentsBusiness intelligence softwarePackages and librariesProgramming languagesDBMS tools
- Identify common coding environments, BI tools, packages, programming languages, and DBMS tools used in data analytics
- Select appropriate tools for given data analysis scenarios
Identify artificial intelligence (AI) concepts
AI and machine learningNatural language processing (NLP)Robotic process automation (RPA)
- Identify AI concepts including generative AI, LLMs, foundational models, and deep learning
- Understand NLP fundamentals and their application to data analysis
- Recognize RPA and automated reporting concepts
2
Data Acquisition and Preparation
22%
Given a scenario, use data acquisition methods
Data integrationSQL queryingBasic query optimizationData collection methods
- Use data acquisition methods including integration, SQL querying, and data collection
- Differentiate between ETL and ELT approaches
- Write and optimize SQL queries with joins, filters, aggregates, and subqueries
- Select appropriate data collection methods for given scenarios
Given a scenario, perform data exploration to identify possible inconsistencies
Data quality issues
- Perform data exploration to identify missing values, duplicates, redundancy, outliers, and completeness issues
- Apply validation techniques to identify data inconsistencies
- Recognize specification mismatches and data type validation errors
Given a scenario, perform appropriate data transformation and cleansing techniques
String manipulation and RegExData conversions and transformationsData cleansing techniques
- Apply string manipulation and RegEx for data cleansing
- Perform data type conversions, binning, scaling, and standardization
- Use imputation techniques for missing data
- Merge, append, parse, and transform datasets appropriately
3
Data Analysis
24%
Given a scenario, select the appropriate communication approach for analysis results
Communication considerations
- Select appropriate communication approaches based on audience, sensitivity, and context
- Adjust the level of detail for technical vs. non-technical stakeholders
- Define and present relevant KPIs for business scenarios
Given a scenario, select the appropriate statistical method or function
Analysis typesMathematical functions - Central tendency and dispersionInferential statisticsLogical, date, and string functions
- Distinguish between descriptive, predictive, prescriptive, and inferential analysis
- Calculate and interpret measures of central tendency and dispersion
- Apply inferential statistical methods including hypothesis testing
- Use logical, date, and string functions appropriately in analysis
Given a scenario, troubleshoot common data analysis issues
Common issuesTroubleshooting approaches
- Troubleshoot common data analysis issues including connectivity, SQL errors, and corrupted data
- Apply systematic troubleshooting approaches using logging, validation, and documentation
4
Visualization and Reporting
20%
Given a scenario, use the appropriate visual elements
Chart typesOther visual elementsDesign elements
- Select appropriate visual elements including charts, maps, pivot tables, and infographics
- Apply design elements such as labels, legends, color schemes, and branding standards
Given a scenario, use the appropriate delivery or consumption method
Report delivery methodsReport cadence and versioningReport types
- Select appropriate delivery methods including dashboards, portals, and summaries
- Determine appropriate report cadence, versioning, and type for given scenarios
Given a scenario, troubleshoot issues using report validation techniques
Common reporting issuesValidation techniques
- Troubleshoot common reporting issues including performance, filters, and data freshness
- Apply validation techniques including code review, peer review, and source validation
5
Data Governance
14%
Explain data management concepts
Data management practices
- Explain data management concepts including documentation, lineage, versioning, and metadata
- Understand the importance of data dictionaries and source of truth
Summarize concepts related to data compliance
Compliance requirements
- Summarize data compliance concepts including retention, GDPR, PCI DSS, audits, and incident reporting
- Apply data classification and ethical data practices
Compare and contrast data privacy and protection practices
Privacy and protection mechanisms
- Compare and contrast data privacy and protection practices including RBAC, encryption, and masking
- Identify and handle PII and PHI appropriately
- Understand NIST framework awareness for data protection
Compare and contrast data quality assurance practices
Quality assurance methodsData quality dimensions and rules
- Compare and contrast data quality assurance practices including testing, monitoring, and profiling
- Evaluate data quality using dimensions such as accuracy, completeness, consistency, and integrity
- Apply automated DQ monitoring and data audit practices
How do I earn this certification?
Passing DA0-002 earns the CompTIA Data+ certification. It sits in the Data and Analytics track.
Next Level Options
- DS0-001 - CompTIA DataSys+Data systems operations - validates ability to deploy, manage, secure, and operate data systems
- DY0-001 - CompTIA DataAI (formerly DataX)Advanced data science and AI - targets professionals with 5+ years experience in data science, machine learning, and AI operations
Alternative Paths
- SY0-701 - CompTIA Security+Complementary security knowledge for data governance and compliance roles
- CV0-004 - CompTIA Cloud+Deeper cloud infrastructure knowledge for cloud-based data analytics
- XK0-006 - CompTIA Linux+Linux skills for data engineering and server-side analytics
- Microsoft Power BI Data Analyst (PL-300) Microsoft-specific BI skills complementing vendor-neutral Data+ knowledge
- Tableau Desktop Specialist Tableau-specific visualization skills complementing Data+ foundation
- AWS Certified Data Engineer - Associate (DEA-C01) AWS-specific data engineering skills for cloud data architecture
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for DA0-002 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2025-10-14
Updates
- Generative AI and Large Language Models New objective 1.5 covers AI concepts including generative AI, LLMs, foundational models, deep learning, NLP, and RPA
- Cloud Platforms (AWS, Azure, GCP) Objective 1.3 now explicitly covers cloud providers and deployment models
- Data Lakehouse Architecture Added as a repository type alongside data lakes, warehouses, and marts in objective 1.2
- Containerization Basic containerization concepts added to infrastructure in objective 1.3
- Modern BI and DBMS Tools Looker, DBeaver, and MongoDB Compass explicitly added to the tools list in objective 1.4
Who should take this exam?
- 18-24 months in a data analyst or similar job role
- Exposure to databases and analytical tools
- Basic understanding of statistics
- Familiarity with data visualization