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CompTIA DataSys+ Syllabus

Data Systems Management

Covers managing data systems, database administration tasks, performance tuning, backup and recovery, and high availability strategies.

26%
Weight
23
Questions
23
Marks

Database Installation and Configuration

  • Database server installation
  • Configuration parameters
  • Instance vs database concepts
  • Connection management
  • Resource management

Performance Management

  • Query optimization and execution plans
  • Indexing strategies (clustered, non-clustered, composite, covering)
  • Statistics and query analyzer
  • Wait types and bottleneck identification
  • Memory and CPU resource management

Backup and Recovery

  • Full, differential, and incremental backups
  • Transaction log backups
  • Point-in-time recovery
  • Recovery models (simple, full, bulk-logged)
  • Backup verification and restore testing

High Availability and Disaster Recovery

  • Database clustering
  • Replication types
  • Always On Availability Groups
  • Log shipping
  • RTO and RPO concepts

Data Governance, Security, and Compliance

Covers data governance frameworks, data security controls, privacy regulations, access management, and data lifecycle management.

25%
Weight
22
Questions
22
Marks

Data Governance Frameworks

  • Data stewardship and ownership
  • Data catalog and metadata management
  • Data lineage
  • Master data management (MDM)
  • Data quality management

Database Security

  • Authentication and authorization
  • Role-based access control (RBAC)
  • Database encryption (TDE, column-level)
  • Data masking and anonymization
  • Auditing and logging

Compliance and Privacy

  • GDPR implications for databases
  • HIPAA data security requirements
  • PCI-DSS for databases
  • Data retention policies
  • Right to erasure/right to be forgotten implementation

Data Analysis and Reporting

Covers data querying with SQL, data transformation, reporting tools, data visualization, and deriving insights from structured data.

20%
Weight
18
Questions
18
Marks

Advanced SQL

  • Joins (INNER, LEFT, RIGHT, FULL, CROSS)
  • Aggregate functions (SUM, COUNT, AVG, MIN, MAX)
  • Window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD)
  • CTEs (Common Table Expressions)
  • Subqueries and derived tables
  • Stored procedures and functions

Data Transformation

  • ETL vs ELT processes
  • Data cleansing techniques
  • Data normalization and denormalization
  • Type conversion and handling NULLs

Reporting and Visualization

  • Report design principles
  • BI tools integration (Power BI, Tableau)
  • Dashboards and KPIs
  • Scheduled and ad-hoc reporting
  • Exporting and distributing reports

Database Management Systems

Covers relational and non-relational database types, schema design, normalization, and the characteristics of major DBMS platforms.

17%
Weight
15
Questions
15
Marks

Relational Databases

  • ACID properties (Atomicity, Consistency, Isolation, Durability)
  • Entity-Relationship (ER) modeling
  • Normalization (1NF, 2NF, 3NF, BCNF)
  • Keys (primary, foreign, composite, surrogate)
  • SQL Server, MySQL, PostgreSQL, Oracle characteristics

Non-Relational Databases

  • NoSQL types: document (MongoDB), key-value (Redis), columnar (Cassandra), graph (Neo4j)
  • CAP theorem (Consistency, Availability, Partition tolerance)
  • BASE vs ACID
  • Use cases for NoSQL vs relational

Data Modeling

  • Conceptual, logical, and physical data models
  • Star and snowflake schema (data warehousing)
  • Dimensional modeling (facts and dimensions)
  • Schema migration and versioning

Data Infrastructure

Covers the underlying infrastructure for data systems, including cloud data platforms, storage technologies, and network considerations for databases.

12%
Weight
11
Questions
11
Marks

Cloud Data Platforms

  • Cloud database services (AWS RDS, Azure SQL Database, Google Cloud SQL)
  • Cloud data warehouses (Redshift, Synapse Analytics, BigQuery)
  • DBaaS trade-offs vs self-managed databases
  • Scaling in cloud (horizontal vs vertical)

Storage and Networking for Databases

  • Storage types (SSD, SAN, NAS) and performance impact
  • IOPS and throughput considerations
  • Network latency and database performance
  • Containerized databases (Docker, Kubernetes)

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Learning Progress

Data Systems Management 85%
Data Governance, Security, and Compliance 92%

Practice Test Scores

95%
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