Microsoft Certified Data Analyst Associate Microsoft Azure Data Fundamentals 4 — Questions and Answers
Question 1: In a star schema, what distinguishes a 'fact table' from a 'dimension table'?
- Fact tables store descriptive attributes; dimension tables store numeric measurements
- Fact tables store numeric measurements and foreign keys; dimension tables store descriptive attributes (Correct answer)
- Fact tables are smaller; dimension tables are larger
- Fact tables use natural keys; dimension tables use surrogate keys
Correct answer: Fact tables store numeric measurements and foreign keys; dimension tables store descriptive attributes
Fact tables contain quantitative measures (like sales amount) and foreign keys to dimension tables, while dimension tables hold descriptive attributes (like product name or customer city).
Question 2: What does OLAP stand for, and what is its primary use case?
- Online Logical Application Processing — managing business logic in real time
- Online Analytical Processing — analyzing large datasets for business intelligence and reporting (Correct answer)
- Operational Logging and Archival Platform — storing audit logs
- On-Premises Legacy Application Protocol — integrating legacy systems
Correct answer: Online Analytical Processing — analyzing large datasets for business intelligence and reporting
OLAP (Online Analytical Processing) is designed for complex analytical queries and multidimensional analysis, enabling fast aggregations and slice-and-dice operations on large historical datasets.
Question 3: Which Azure service provides a fully managed, scalable Apache Kafka service?
- Azure Service Bus
- Azure Event Hubs with Kafka protocol support (Correct answer)
- Azure Queue Storage
- Azure Logic Apps
Correct answer: Azure Event Hubs with Kafka protocol support
Azure Event Hubs supports the Apache Kafka protocol, allowing existing Kafka applications to connect without changing client code.
Question 4: What is 'data lineage' in the context of data governance?
- The hierarchical ownership structure of a database
- The historical record of data's origins and how it has moved and transformed over time (Correct answer)
- The chronological list of schema changes to a table
- The audit log of who queried which data
Correct answer: The historical record of data's origins and how it has moved and transformed over time
Data lineage tracks the journey of data from its origin through all transformations and movements, helping organizations understand data quality and impact analysis.
Question 5: In Azure SQL Database, what is the purpose of 'elastic pools'?
- Automatically scaling a single database's CPU during peak load
- Sharing a fixed set of compute resources across multiple databases to optimize cost (Correct answer)
- Replicating a database across multiple Azure regions
- Partitioning a large table across multiple storage files
Correct answer: Sharing a fixed set of compute resources across multiple databases to optimize cost
Elastic pools allow multiple Azure SQL databases to share a pool of eDTUs or vCores, which is cost-effective when databases have unpredictable or varying usage patterns.
Question 6: Which concept describes the practice of keeping copies of data at multiple geographic locations to improve availability and disaster recovery?
- Data partitioning
- Data replication (Correct answer)
- Data archiving
- Data deduplication
Correct answer: Data replication
Data replication involves maintaining synchronized copies of data in multiple locations, ensuring availability and enabling disaster recovery if one location fails.
Question 7: What is the primary advantage of using columnar storage format (like Parquet) over row-based storage for analytical workloads?
- Faster individual record inserts and updates
- Efficient compression and faster reads when querying only specific columns (Correct answer)
- Better support for foreign key constraints
- Easier integration with OLTP applications
Correct answer: Efficient compression and faster reads when querying only specific columns
Columnar formats store data by column rather than by row, enabling analytical queries that only read relevant columns to skip irrelevant data and benefit from better compression ratios.
Microsoft Certified Data Analyst Associate Microsoft Azure Data Fundamentals 4
In a star schema, what distinguishes a 'fact table' from a 'dimension table'?