DA0-002 CompTIA Data+ Certification Practice Test 2026

DA0-002 CompTIA Data+ Certification Practice Test 2026

CompTIA Data+ is an early-career data analytics certification that shows you have the practical skills to turn raw data into meaningful insights. Gain the confidence to analyze, interpret, and communicate data clearly, so you can solve real business problems, stand out to employers, and help your organization make smarter, data-driven decisions.

Data+ exam objectives summary :

Data concepts and environments (20%)

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

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

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

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

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

Data acquisition and preparation (22%)

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

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

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

Data analysis (24%)

• Communicate analysis results: Select methods for different audiences.

• Select statistical methods: Apply basic statistical techniques to data.

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

Visualization and reporting (20%)

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

• Deliver reports: Provide dashboards or summaries using appropriate methods.

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

Data governance (14%)

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

• Summarize compliance requirements: Retention, audits, and regulations.

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

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