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WorksheetsBNI DE Day 4
Total questions: 20
Worksheet time: 20mins
What is the central component in a star schema?
Fact table
Dimension table
Snowflake table
Normalized table
Normalized tableIn a snowflake schema, how are dimension tables structured?
Denormalized
Centralized
Normalized
Clustered
What is the primary benefit of a star schema for query performance?
Enhanced data governance
Simplified data maintenance
Optimized indexing
Reduced number of joins
Which type of schema is more suitable when storage efficiency is a top priority?
Star schema
Snowflake schema
Both are equally efficient
Neither affects storage efficiency
What is the purpose of hierarchies in dimensional modeling?
To increase redundancy
To simplify data maintenance
To provide context to the data
To facilitate drilling down or rolling up in analysis
In a snowflake schema, what does normalization aim to achieve?
Increased redundancy
Simplified data structure
Enhanced data integrity
Faster query performance
Which schema is more user-friendly for self-service business intelligence?
Star schema
Snowflake schema
Both are equally user-friendly
Neither supports self-service BI
What is the main drawback of a snowflake schema in terms of query performance?
Increased data redundancy
Complexity in data maintenance
Slower query performance
Slower query performance
Which schema is preferred when stringent data governance requirements exist?
Star schema
Snowflake schema
Both are equally suitable
Neither addresses data governance
What is the primary goal of normalizing dimension tables in a snowflake schema?
To increase redundancy
To improve data integrity
To simplify query performance
To enhance data governance
In a star schema, what is the central entity for analysis?
Fact table
Snowflake table
Dimension table
Clustered table
What is the purpose of conformed dimensions in dimensional modeling?
To increase data redundancy
To enhance data governance
To ensure consistency across data marts
To simplify data maintenance
Which schema is more space-efficient due to denormalization?
Star schema
Snowflake schema
Both have equal space efficiency
Neither affects space efficiency
What does "drilling down" in analysis refer to?
Navigating from a more detailed level to a higher-level summary
Aggregating data to a more detailed level
Breaking down data into multiple related tables
Enhancing data integrity through normalization
In a snowflake schema, what does each sub-table represent?
A separate fact table
A higher-level summary
A level of the dimension's hierarchy
A level of the dimension's hierarchy
What is the primary consideration when choosing a star schema?
Enhanced data governance
Simplified data maintenance
Optimized query performance
Normalization of dimension tables
Which schema might be preferred in a scenario where product details are highly complex?
Star schema
Snowflake schema
Both are equally suitable
Neither addresses product complexity
What is the primary advantage of a star schema for end-users?
Enhanced data governance
Simplified query performance
Intuitive and user-friendly navigation
Reduced complexity in data maintenance
In which schema might you find a fact table directly connected to dimension tables?
Star schema
Snowflake schema
Both have similar structures
Neither involves connecting tables
What does "rolling up" in analysis involve?
Navigating from a higher-level summary to a more detailed level
Aggregating data to a higher-level summary
Breaking down data into multiple related tables
Extracting data from the database for analysis
