CompTIA DataSys+ is designed for professionals with 2–3 years of database administration experience who are ready to manage today’s evolving data environments. It validates your ability to design, deploy, manage, secure and integrate relational and non-relational databases across on-premises, cloud and hybrid environments, preparing you to support the resilient data systems that power modern applications, analytics and AI. The certification is expected to launch on or around October 13, 2026.
V2
DataSys+ V2 (Coming October 2026)
Skills you'll learn
Build modern database administration skills with CompTIA learning and validate them with DataSys+ certification.
Work with relational and non-relational databases, data types, SQL, scripting and programming concepts.
Plan, design, deploy and validate databases across on-premises, cloud and hybrid environments.
Monitor database health, optimize performance and complete essential maintenance and data management tasks.
Protect data through encryption, access controls, governance, auditing and current database security practices.
Implement backup, restoration, disaster recovery, high availability and fault-tolerance strategies.
Acquire, connect and troubleshoot data across different sources, formats and systems while understanding emerging AI concepts.
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Exam details
Exam version: V2
Exam series code: DS0-002
Launch date: October 13, 2026
Number of questions: Maximum of 90
Types of questions: Multiple-choice and performance-based
Duration: 90 minutes
Passing score: 700 on a scale of 100 to 900
Languages: English
Recommended experience: Database administrator with 2 to 3 years of hands-on experience
Retirement: Estimated 3 years after launch
DataSys+ (V2) exam objectives summary
Database Fundamentals (19%)
- Compare and contrast database types and data types: Relational, non-relational, structured, unstructured and semistructured data.
- Develop, modify, and run Structured Query Language (SQL) code: Queries, joins, CRUD operations, stored procedures and functions.
- Compare and contrast scripting methods and environments: Server-side and client-side scripting using common database tools and languages.
- Explain the impact of programming on database performance: ORM frameworks, generated SQL and database optimization.
Database Deployment (17%)
- Compare and contrast aspects of database planning and operations: Requirements gathering, system design, validation and testing.
- Implement techniques related to database design and documentation: Schemas, data dictionaries, entity relationship diagrams and SOPs.
- Explain connectivity concepts related to databases: Networking, cloud environments, load balancing and database communications.
Database Management and Maintenance (18%)
- Explain the purpose of monitoring and reporting for database management and performance: Alerts, utilization, connections and system health.
- Understand common database maintenance processes: Patching, integrity checks, log reviews and performance tuning.
- Implement data management tasks: Data structures, relationships, indexing and normalization.
Data and Database Security (19%)
- Understand data security concepts: Encryption, key management, masking and auditing.
- Explain the purpose of governance and regulatory compliance: Data retention, privacy requirements and regulatory standards.
- Implement policies and best practices: Authentication, authorization and identity management.
- Know the purpose of database security: Zero Trust principles, vulnerability management and threat prevention.
Business Continuity (14%)
- Implement backup and recovery processes: Backup strategies, validation and restoration.
- Understand the importance of disaster recovery (DR) and best practices: Recovery testing, failover and recovery objectives.
- Compare and contrast fault tolerance operations: High availability, redundancy and replication.
Data Integration (13%)
- Use data acquisition techniques and methods: ETL/ELT processes, data sources and connectivity methods.
- Troubleshoot common data acquisition issues: Data quality, schema mismatches and connectivity challenges.
- Explain emerging technologies and AI concepts related to data integration: AI concepts, machine learning tools and automation.