CMA Data Architecture & Management 2 — Questions and Answers
Question 1: Which data integration pattern is most appropriate for synchronizing large volumes of data between enterprise systems on a scheduled basis?
- Event-driven streaming via a message broker
- Batch ETL (Extract, Transform, Load) processing (Correct answer)
- Synchronous REST API integration
- Manual data reconciliation and file transfer
Correct answer: Batch ETL (Extract, Transform, Load) processing
Batch ETL is designed to extract, transform, and load large volumes of data at scheduled intervals, making it well-suited for high-volume, time-insensitive data synchronization workloads.
Question 2: What is the key architectural difference between a data lake and a data warehouse?
- A data lake stores only structured data while a data warehouse stores unstructured data
- A data lake stores raw data in native format while a data warehouse stores processed, structured data (Correct answer)
- A data lake enforces schema at load time while a data warehouse uses schema-on-read
- A data lake is exclusively for real-time analytics while a data warehouse is batch-only
Correct answer: A data lake stores raw data in native format while a data warehouse stores processed, structured data
A data lake stores raw data in native format (structured, semi-structured, unstructured) using schema-on-read, while a data warehouse uses schema-on-write with processed, integrated, structured data.
Question 3: What does the CAP theorem state about distributed data systems?
- A distributed system cannot simultaneously guarantee Consistency, Availability, and Partition tolerance (Correct answer)
- Data Capacity, Access speed, and Performance are the three pillars of database design
- Caching, APIs, and Processing are the three components of a modern data pipeline
- Cloud, on-premises, and hybrid are the three viable deployment options for enterprise data
Correct answer: A distributed system cannot simultaneously guarantee Consistency, Availability, and Partition tolerance
The CAP theorem states that a distributed data system can only guarantee two of three properties simultaneously: Consistency, Availability, and Partition tolerance.
Question 4: What is the primary advantage of a data mesh architecture approach?
- Centralizing all data processing under a single enterprise data engineering team
- Enabling domain teams to own, manage, and serve their data as products (Correct answer)
- Replacing all existing data warehouses with a unified cloud data lake
- Eliminating the need for enterprise-wide data governance policies
Correct answer: Enabling domain teams to own, manage, and serve their data as products
Data mesh decentralizes data ownership to domain teams who treat data as products, improving scalability, agility, and the application of domain expertise to data quality and management.
Question 5: Which integration approach best supports real-time data flow between microservices with loose coupling?
- Nightly batch ETL jobs between service databases
- Event streaming using a message broker such as Apache Kafka (Correct answer)
- Scheduled full-table database replication
- Synchronous REST API calls with retry logic
Correct answer: Event streaming using a message broker such as Apache Kafka
Event streaming with message brokers like Apache Kafka enables real-time, decoupled data integration through publish-subscribe patterns, ensuring services remain independent and scalable.
Question 6: What is the primary purpose of a data catalog in enterprise architecture?
- To store encrypted backup copies of all enterprise databases
- To provide a searchable inventory of data assets with metadata, lineage, and business context (Correct answer)
- To enforce row-level data access controls across all source systems
- To transform and cleanse data before loading it into the data warehouse
Correct answer: To provide a searchable inventory of data assets with metadata, lineage, and business context
A data catalog provides a centralized, searchable inventory of data assets enriched with metadata, lineage, and business context, enabling data discovery and supporting governance.
Question 7: In data architecture, what is 'data virtualization'?
- Creating virtual machines dedicated to hosting database servers
- Providing unified, real-time data access across multiple sources without physical data movement (Correct answer)
- Compressing data to reduce on-disk storage requirements
- Replicating data to geographically distributed disaster recovery sites
Correct answer: Providing unified, real-time data access across multiple sources without physical data movement
Data virtualization provides an abstraction layer enabling unified, real-time data access across heterogeneous sources without physically moving or replicating the underlying data.
CMA Data Architecture & Management 2
Which data integration pattern is most appropriate for synchronizing large volumes of data between enterprise systems on a scheduled basis?