Analytics Data Management Practice Exam
Analytics Data Management Practice Exam
About Analytics Data Management Exam
The Analytics Data Management Exam assesses your skills in handling data across its lifecycle—from ingestion and integration to storage, processing, and governance—in support of analytics and business intelligence (BI) initiatives. Ideal for professionals involved in data strategy, this exam emphasizes the technical and organizational methods used to ensure data quality, consistency, and accessibility for informed decision-making. Candidates will gain insights into data warehousing, ETL processes, metadata management, data lakes, and regulatory compliance practices. Mastery of analytics data management enables organizations to derive meaningful insights while ensuring data integrity and compliance.
Who should take the Exam?
This exam is ideal for:
- Data analysts and data engineers
- Business intelligence professionals
- Data architects and IT managers
- Data governance and compliance officers
- Students and learners aiming for data-related roles
Skills Required
- Understanding of databases and data warehouses
- Familiarity with ETL tools and processes
- Knowledge of data quality and governance principles
- Analytical thinking and problem-solving
Knowledge Gained
- Data lifecycle and data architecture principles
- Designing ETL pipelines and data integration strategies
- Implementing data governance and compliance controls
- Techniques for ensuring data quality and consistency
Course Outline
The Analytics Data Management Exam covers the following topics -
Domain 1 – Introduction to Data Management for Analytics
- Purpose and scope of analytics data management
- Data lifecycle overview
- Roles and responsibilities in data teams
Domain 2 – Data Integration and ETL Processes
- Data ingestion techniques
- ETL/ELT concepts and tools
- Real-time vs. batch data processing
Domain 3 – Data Storage and Architecture
- Data warehouses and data lakes
- Cloud vs. on-premise storage solutions
- Scalability and performance considerations
Domain 4 – Data Quality and Profiling
- Dimensions of data quality
- Data profiling techniques
- Data cleansing and standardization
Domain 5 – Metadata and Master Data Management
- Metadata definitions and usage
- MDM strategies and frameworks
- Data cataloging and lineage tracking
Domain 6 – Data Governance and Compliance
- Data governance frameworks (e.g., DAMA-DMBOK)
- Regulatory requirements (GDPR, HIPAA)
- Policy enforcement and audit trails
