CompTIA Data+ (DA0-002) Practice Exam
CompTIA Data+ (DA0-002) Practice Exam
About CompTIA Data+ (DA0-002) Exam
CompTIA Data+ is an entry-level, vendor-neutral certification that validates your ability to work across the full data life cycle, from collecting and preparing data to analyzing, visualizing, and governing it in support of real business decisions. It’s designed to demonstrate to employers that you can transform raw data into actionable insights, select the appropriate analytical methods, and communicate results clearly and confidently. This certification reflects the evolving demands of data analytics in modern workplaces.
Target Audience
This certification is a strong fit for:
- Early-career professionals building data analytics expertise
- IT staff, business analysts, and reporting specialists moving into analytics
- Professionals in roles like junior data analyst, operations analyst, or marketing analyst
- Anyone looking to validate data skills without being tied to a particular vendor or tool
- Those aiming to support data-driven decision-making in their team or organization
Skills Required
Before attempting the exam, candidates should be comfortable with:
- Basic understanding of data types, sources, and structures
- Exposure to analytics tools or environments (spreadsheets, BI dashboards, SQL interfaces)
- Fundamental statistical concepts and reasoning
- Working with data sets in preparation and analysis contexts
- Translating business questions into analytical approaches
- Hands-on experience with data reporting or analytics, even at an entry level, is beneficial.
Knowledge Gained
Earning this certification helps you build practical data expertise, including the ability to:
- Understand core data concepts and environments
- Collect, cleanse, and prepare data from diverse sources
- Apply statistical methods to uncover patterns and insights
- Create meaningful visualizations and reports that aid decision-making
- Apply governance, data quality, and compliance practices to analytics
- Use industry-standard tools to support analytical workflows
Exam Format
- Number of Questions: Up to 90 questions
- Duration: 90 minutes
- Question Types: Multiple-choice and performance-based items
- Delivery: Computer-based, proctored exam through approved testing providers
Course Outline
The CompTIA Data+ (DA0-002) Exam covers the following topics -
Domain 1. Data Concepts and Environments (20%)
1.1 Core Data Concepts
- Data types and structures (relational, non-relational)
- File formats and common extensions
- Data schemas, dimensions, and relationships
1.2 Data Storage and Systems
- Databases, data lakes, warehouses
- Cloud, on-premises, hybrid environments
1.3 Data Sources and Tools
- Logs, APIs, files, repositories
- Overview of analytics and BI platforms
1.4 AI and Emerging Concepts in Data
- AI models and natural language processing basics
- Robotic automation and data workflows
Domain 2. Data Acquisition and Preparation (22%)
2.1 Data Collection Methods
- Integration and extraction techniques
- Use of ETL/ELT processes
2.2 Data Exploration and Quality Checks
- Identifying missing values, redundancies, outliers
- Dealing with duplicates and invalid entries
2.3 Cleansing and Transformation
- Data parsing, formatting, and normalization
- Merging, concatenation, imputation
2.4 Preparing Data for Analysis
- Structuring data sets for downstream tasks
- Ensuring consistency and readiness for analytics
Domain 3. Data Analysis (24%)
3.1 Selecting Analytical Techniques
- Descriptive vs. inferential methods
- Choosing the right statistic for the question
3.2 Applying Statistical Methods
- Central tendency (mean, median, mode)
- Dispersion (variance, standard deviation)
3.3 Pattern Detection and Interpretation
- Correlation and regression basics
- Recognizing trends and relationships
3.4 Communicating Analytical Insights
- Making data decisions based on analysis results
- Defining relevant and actionable output
Domain 4. Visualization and Reporting (20%)
4.1 Visualization Fundamentals
- Choosing the right chart type (bar, line, scatter, etc.)
- Dashboards and summary tables
4.2 Report Design and Delivery
- Communicating results for business audiences
- Organization and layout best practices
4.3 Validating Visuals
- Ensuring accuracy of graphs and dashboards
- Reviewing reports for clarity and correctness
Domain 5. Data Governance, Quality, and Controls (14%)
5.1 Governance Frameworks
- Policies for data stewardship and documentation
- Versioning and data lineage tracking
5.2 Compliance and Privacy Standards
- Regulatory principles for safe data use
- Roles and responsibilities in governance
5.3 Quality Assurance Practices
- Profiling and monitoring for data accuracy
- Data protection and access controls
