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Exam Information
Official specifications published by CompTIA
Exam Format
90 min
90
675
Entry-Level
Registration
$239 USD
Pearson VUE or online proctoring
English, Japanese, Thai
Validity
3 years
Pass the current CompTIA Data+ exam before expiration; Earn 50 Continuing Education Units (CEUs) through CompTIA CEU program; Complete higher-level CompTIA certifications
DA0-001 Exam Topics and Domains
DA0-001 is organized into 5 weighted domains. Expect to work with Informatica Data Quality, Talend Data Quality, Alation, Amazon Redshift, and more.
1
Data Concepts and Environments
15%
Identify basic concepts of data schemas and dimensions
Database TypesData Warehousing ConceptsData Lake ConceptsSchema ConceptsKeys and Constraints
- Identify and compare relational and non-relational databases
- Understand data warehousing and data lake concepts
- Recognize different schema types (star, snowflake)
- Apply primary and foreign key concepts to maintain data integrity
Compare and contrast different data types
Temporal Data TypesNumeric Data TypesText and String DataSpecial Data Types
- Compare and contrast numeric, text, date, and boolean data types
- Select appropriate data types for different scenarios
- Understand special data types like currency and phone numbers
Compare and contrast common data structures and file formats
Structured Data FormatsSemi-Structured and Unstructured DataFile Format Comparison
- Distinguish between structured, semi-structured, and unstructured data
- Compare CSV, XML, and JSON file formats
- Select appropriate file formats for different use cases
2
Data Mining
25%
Explain data acquisition concepts
Data Integration MethodsData Collection MethodsData Sources
- Explain ETL and ELT processes and their differences
- Identify appropriate data collection methods for scenarios
- Understand various data acquisition techniques including APIs and web scraping
Identify common reasons for cleansing and profiling datasets
Data Quality IssuesData Profiling
- Identify data quality issues including duplicates, missing values, and invalid data
- Understand the importance of data profiling
- Apply data validation rules to ensure quality
Given a scenario, execute data manipulation techniques
Data TransformationData CombinationData Cleaning Operations
- Execute data transformation techniques like recoding and normalization
- Combine data using merge, blend, and concatenation
- Clean data using imputation, aggregation, and parsing
Explain common techniques for data manipulation and query optimization
Query Optimization TechniquesSQL Best Practices
- Explain query optimization techniques including indexing
- Apply SQL best practices for performance
- Use temporary tables and subqueries appropriately
3
Data Analysis
23%
Given a scenario, apply the appropriate descriptive statistical methods
Measures of Central TendencyMeasures of DispersionPercentages and Frequencies
- Apply measures of central tendency (mean, median, mode)
- Calculate measures of dispersion (range, variance, standard deviation)
- Compute percentages, percent change, and confidence intervals
Explain the purpose of inferential statistical methods
Hypothesis TestingStatistical TestsRegression and Correlation
- Explain hypothesis testing and p-values
- Apply t-tests, Z-scores, and chi-squared tests
- Understand simple linear regression and correlation analysis
Summarize types of analysis and key analysis techniques
Analysis TypesAnalysis Techniques
- Summarize descriptive, predictive, and prescriptive analysis
- Apply trend, performance, and exploratory analysis techniques
- Distinguish between different analysis approaches
Identify common data analytics tools
Programming and Scripting ToolsEnterprise Analytics ToolsBusiness Intelligence Tools
- Identify common programming tools for data analysis (Python, R, SQL)
- Recognize enterprise analytics platforms (SAS, SPSS, Cognos)
- Understand business intelligence tools (Tableau, Power BI, Qlik)
4
Visualization
23%
Given a scenario, translate business requirements to form appropriate visualizations
Understanding Audience NeedsChart Type Selection
- Translate business requirements into visualization needs
- Identify appropriate chart types for different scenarios
- Understand audience and stakeholder requirements
Given a scenario, use appropriate design components for reports and dashboards
Visual Design ElementsInteractive Components
- Apply appropriate design components including color, font, and layout
- Use corporate branding in reports and dashboards
- Implement interactive elements like filters and parameters
Given a scenario, use appropriate methods for dashboard development
Dashboard PlanningDashboard Development and Deployment
- Plan dashboards using wireframes and mockups
- Develop and deploy dashboards effectively
- Optimize dashboards for different devices
Given a scenario, apply the appropriate type of visualization
Comparison ChartsTrend and Time SeriesDistribution and CompositionRelationship and CorrelationSpecialized Visualizations
- Apply appropriate chart types for different data scenarios
- Use line charts, bar charts, and pie charts effectively
- Create scatter plots, heat maps, and geographic visualizations
- Select specialized visualizations like tree maps and word clouds
Compare and contrast types of reports
Report CategoriesReport Purposes
- Compare static and dynamic reports
- Distinguish between ad-hoc, scheduled, and self-service reports
- Understand operational, analytical, and compliance reports
5
Data Governance, Quality, and Controls
14%
Summarize important data governance concepts
Data Governance FrameworkData Access and SecurityData Storage and Jurisdiction
- Summarize data governance framework components
- Understand data stewardship and ownership
- Explain data access, security, and jurisdiction requirements
Apply data quality control concepts
Data Quality DimensionsData Quality Rules and Metrics
- Apply data quality dimensions (accuracy, completeness, consistency)
- Implement data quality rules and validation
- Use data quality metrics and automated checks
Explain master data management (MDM) concepts
MDM Core ConceptsMetadata Management
- Explain master data management concepts including golden record
- Understand data dictionary, catalog, and lineage
- Apply MDM concepts for data consolidation
Given a scenario, apply fundamentals of data privacy and ethics
Sensitive Data TypesPrivacy Protection TechniquesCompliance and Ethics
- Identify PII, PHI, and other sensitive data types
- Apply data anonymization and encryption techniques
- Understand GDPR, data retention, and ethical data use
How do I earn this certification?
Passing DA0-001 earns the CompTIA Data+ certification. It sits in the Data Analytics track.
Current Level Exams
Next Level Options
- PT0-003 - CompTIA PenTest+Data analysis skills applicable to security analytics
- CS0-003 - CompTIA CySA+Security data analytics career path
- CASP-005 - CompTIA CASP+Advanced security analytics and architecture
Alternative Paths
- SY0-701 - CompTIA Security+Complementary security knowledge for data protection
- N10-009 - CompTIA Network+Understanding network data flow and infrastructure
- CAS-004 - CompTIA CASP+Enterprise-level security and risk analysis
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
Use the official PlanetCert Practice Test alongside the study plan below to prepare efficiently for DA0-001.
What's changed on this exam?
Current Status
- ACTIVE_RETIRING_SOON
- Last content update: 2022-02-28
- Announcement date: 2025-08-01
Updates
- Cloud Data Platforms Current cloud services While DA0-001 is vendor-neutral, understanding cloud data warehouses (Snowflake, BigQuery, Redshift) is beneficial
- AI and Machine Learning Integration DA0-001 covers basic regression/correlation; AI/ML tools more prominent in DA0-002
- Data Visualization Tools
- Python and R for Data Analysis
- Data Privacy Regulations
Who should take this exam?
- 18-24 months in a report/business analyst job role
- Exposure to databases and analytical tools
- Basic understanding of statistics
- Data visualization experience