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WorksheetsDBMS Group 8 Review Quiz
Total questions: 15
Worksheet time: 3mins
Why are conformed dimensions important in a data warehouse?
They allow different fact tables to share the same business meaning
They increase data redundancy for better storage optimization
They separate unrelated business processes in different data marts
They are used only in OLTP databases
Which of the following is NOT a valid reason for using multiple fact tables in a star schema?
Different levels of data aggregation
Storing different business processes
Improving query performance
Reducing data redundancy by eliminating dimension tables
What is the primary difference between a fact table and a dimension table?
Fact tables store descriptive data, while dimension tables store numerical data
Fact tables store business events, while dimension tables provide context
Fact tables always contain text-based information
Dimension tables store aggregated data
Why is a helper table more scalable than a denormalized table?
It stores multiple values in a single column
It eliminates data redundancy and allows unlimited relationships
It increases the size of the database significantly
It requires modifying the entire row for every update
What is the primary advantage of using a helper table for many-to-many relationships?
It reduces the number of queries needed
It stores all possible values in one row
It makes data retrieval faster and updates more efficient
It removes the need for indexing
In a student-course database, how would you efficiently store a many-to-many relationship?
By adding multiple course columns in the student table
By storing all courses in a single text column separated by commas
By using a helper table to track student-course relationships
By creating a separate table for each student
What is a factless fact table?
A table that stores numerical measures like sales amounts.
A table that tracks events or coverage using foreign keys but no measures
A table that only stores descriptive data about dimensions.
A table that stores aggregated data for reporting.
Which of the following is a common use case for a factless fact table?
Tracking student attendance in classes
Storing sales revenue and quantitiesCalculating profit margins
Calculating profit margins
Storing customer addresses
What is the primary purpose of foreign keys in a factless fact table?
To store numerical data like sales amounts
To store descriptive data about business processes
To perform calculations like sums and averages
To link to dimension tables and provide context for the data
What is the primary purpose of a hierarchy in data warehousing?
To store all data in a single table
To increase redundancy in data storage
To allow users to drill down or roll up data for analysis at different levels of granularity
To replace fact tables in a database
What is a major drawback of storing all hierarchy levels in a single denormalized table?
It simplifies database queries
It increases redundancy and update anomalies
It eliminates the need for indexing
It ensures data integrity and consistency
In a normalized product hierarchy, which level of the hierarchy is typically associated with the fact table?
Product Group
Product Category
Product Family
Product Dimension
In a recursive relationship within a data warehouse, which technique is most effective for optimizing query performance while maintaining hierarchy integrity?
Using self-joins extensively
Creating bridge tables with depth indicators
Applying surrogate keys and bridge tables
Implementing dynamic SQL for each hierarchy level
When designing a helper table for handling recursive hierarchies, why is a "depth" indicator crucial?
It eliminates the need for fact tables
It defines the cardinality of the fact table
It optimizes aggregation and roll-up operations
It helps normalize data by removing redundancy
In a snowflake schema with a complex hierarchy, which of the following challenges is most likely to arise?
Managing slowly changing dimensions
Difficulty in writing efficient SQL queries due to multiple joins
Inability to handle recursive relationships
Redundant data storage leading to inconsistency
