Worksheetsdemo_D&A_monthlymeeting
Total questions: 10
Worksheet time: 2mins
1: Why do organizations need data warehouses?
A) For structured, optimized analytical queries
B) To ensure data consistency and quality
C) For complex business intelligence reporting
D) To replace all other data storage systems
2. What are advantages of cloud data warehouses over traditional ones?
A. Cloud warehouses scale elastically
B. Traditional warehouses run on smartphones
C. Cloud options reduce hardware maintenance
D. Traditional ones are open source
3: When comparing SQL native vs Spark/Python native environments, which is true?
A) SQL native is better for traditional BI users
B) Spark/python native is better for data scientists and engineers
C) Both can coexist in a lakehouse
D) You must choose only one approach
4. What is a data lakehouse designed to combine?
A. Structured, unstructured, and semi-structured data
B. Real estate features like apartments and condos
C. Benefits of data warehouses and data lakes
D. NoSQL and Excel spreadsheets
5. What’s a key reason companies are switching to lakehouse architectures?
A. They unify AI, BI, and data engineering
B. They only store structured data
C. They support real-time and historical analytics
D. They replace cloud entirely
6: What is a decoupled architecture in data lakehouse context?
A) Storage and compute are tightly integrated
B) Storage and compute can scale independently
C) Like a high-story apartment where each floor serves different purposes
D) All components must be deployed together
7: What are the main benefits of schema evolution in a data lakehouse?
A) Maintains business continuity during schema changes
B) Eliminates the need for data validation
C) Reduces risk of system downtime
D) Automatically fixes data quality issues
8: What is "time travel" in data lakehouse context?
A) Predicting future data trends
B) Accessing historical versions of data
C) Synchronizing data across time zones
D) Querying data as it existed at specific points in time
9: What helps non-technical users benefit from a lakehouse?
A) SQL-native query interfaces that work like traditional databases
B) Self-service BI tools with drag-and-drop functionality
C) Pre-built dashboards and reports accessible through web browsers
D) Direct access to Python and Spark programming environments
E) Automated data governance and quality controls running in the background
10: When might you choose a data warehouse over a lakehouse?
A) When you need case-insensitive SQL queries
B) When you require ACID transactions across multiple tables
C) When you only work with structured data
D) When you need the lowest possible storage costs
