MCQs

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Overall MCQs for Reference

What is a star schema primarily used for in artificial intelligence and analytics?
a. To store transactional data only
b. To organize and model data for reporting and analysis
c. To enforce database normalization
d. To store images for machine learning
Answer: b. To organize and model data for reporting and analysisdatabricks+2
What is the central table in a star schema called?
a. Attribute table
b. Join table
c. Fact table
d. Index table
Answer: c. Fact tableshieldbase+2
What type of data does the fact table in a star schema contain?
a. Categorical data
b. Document data
c. Numerical measures or facts
d. Only date information
Answer: c. Numerical measures or factstechtarget+2
What surrounds the fact table in a star schema?
a. Indexes
b. Dimension tables
c. View tables
d. Procedures
Answer: b. Dimension tablesowox+2
What is a key characteristic of a star schema’s dimension table?
a. Normalized relational data
b. Hierarchies split into multiple tables
c. Denormalized attributes describing the data
d. Stores only surrogate keys
Answer: c. Denormalized attributes describing the datalearn.microsoft+2
Which schema organizes data in a denormalized way for fast queries?
a. Snowflake schema
b. Star schema
c. Relational schema
d. Tree schema
Answer: b. Star schemabuiltin+2
What kind of queries benefit most from a star schema?
a. Complex transactional updates
b. High-speed aggregation and OLAP queries
c. Frequent schema changes
d. Document retrieval queries
Answer: b. High-speed aggregation and OLAP queriesmatillion+2
When are surrogate keys typically used in a star schema?
a. Only in fact tables
b. In both fact and dimension tables
c. Only in dimension tables
d. Never used
Answer: c. Only in dimension tablesairbyte+2
Which attribute usually DOES NOT belong in a dimension table of a star schema?
a. Description
b. Hierarchy level
c. Measurement
d. Category
Answer: c. Measurementwikipedia+2
What is an example of a fact in a star schema?
a. Product color
b. Customer ID
c. Sales amount
d. Country name
Answer: c. Sales amountshieldbase+2
What is an example of a dimension in a star schema?
a. Order total
b. Time
c. Transaction count
d. Average price
Answer: b. Timelearn.microsoft+2
Which best describes the relationship between fact and dimension tables in a star schema?
a. One-to-one
b. One-to-many (from dimension to fact)
c. Many-to-many
d. Hierarchical
Answer: b. One-to-many (from dimension to fact)wikipedia+2
What is a major advantage of star schema for analysts?
a. Data is always normalized
b. Simple and easily understood structure
c. Minimizes storage usage
d. Ideal for small data volumes only
Answer: b. Simple and easily understood structurebuiltin+2
Which business scenario is a common use case for star schemas?
a. Real-time transaction processing
b. Sales and marketing analytics
c. Operating system records
d. Video data storage
Answer: b. Sales and marketing analyticskyvosinsights+2
Why is query performance typically better with star schemas than with more normalized schemas?
a. More tables are joined
b. Data duplication is minimized
c. Queries require fewer joins
d. OLTP transactions are faster
Answer: c. Queries require fewer joinsthoughtspot+2
What is the main disadvantage of star schemas in data warehousing?
a. Complex dimension hierarchies required
b. Increased data redundancy
c. Difficult to understand
d. Inaccurate query results
Answer: b. Increased data redundancygeeksforgeeks+2
Which component in star schema design aids in backtracking data origins and data lineage?
a. Surrogate keys in dimension tables
b. Foreign keys in fact tables
c. Indexes on all columns
d. Document storage
Answer: a. Surrogate keys in dimension tablesairbyte+1
What is a primary feature of dimension tables in a star schema?
a. Store facts only
b. Contain descriptive information
c. Must be normalized
d. Used solely for indexing
Answer: b. Contain descriptive informationdatabricks+2
What limitations can star schema encounter with growing complexity?
a. Hard to scale and modify dynamically
b. Flexible to relationships change
c. Unlimited dimension support
d. Automatic normalization
Answer: a. Hard to scale and modify dynamicallygeeksforgeeks+1
In artificial intelligence, why are star schemas helpful for machine learning pipelines?
a. Normalizes training data
b. Simplifies feature engineering with descriptive dimensions
c. Reduces dimensionality for neural networks
d. Encrypts all data by default
Answer: b. Simplifies feature engineering with descriptive dimensionsdatabricks+1
Star schemas are particularly optimized for which type of database operation?
a. Insert operations
b. Analytical queries
c. Index creation
d. Backup and restore
Answer: b. Analytical queriesdatacamp+2
What is the primary key structure of a fact table in a star schema?
a. Composite key from dimensions
b. Natural key
c. Surrogate key only
d. No key needed
Answer: a. Composite key from dimensionstechtarget+1
Which scenario might expose the weakness of excessive data redundancy in star schemas?
a. Data aggregation queries
b. Frequent updates to dimension attributes
c. Reporting user counts
d. Application logging
Answer: b. Frequent updates to dimension attributeskyvosinsights+1
Which of these best describes the visualization of a star schema?
a. Linear chain of tables
b. Tree structure with roots
c. Central table with radiating dimension tables
d. Interconnected mesh
Answer: c. Central table with radiating dimension tablesshieldbase+2
Which schema type is often compared with star schema for business intelligence applications?
a. Mesh schema
b. Tree schema
c. Snowflake schema
d. Flat file schema
Answer: c. Snowflake schemasimplilearn+2
In star schemas, which table typically grows fastest in size over time?
a. Dimension table
b. Index table
c. Fact table
d. Metadata table
Answer: c. Fact tabletechtarget+2
How does star schema affect storage requirements compared to normalized schemas?
a. Storage is typically more due to denormalization
b. Always uses less storage
c. Needs external memory
d. No effect on storage
Answer: a. Storage is typically more due to denormalizationthoughtspot+2
Which star schema aspect is crucial for building time-based analytics?
a. Product dimension
b. Date dimension
c. Fact detail granularity
d. Surrogate keys only
Answer: b. Date dimensionlearn.microsoft+1
How are many-to-many relationships generally handled in star schema design?
a. Through bridge tables or not at all
b. In the fact table only
c. Ignored by design
d. Stored in each dimension
Answer: a. Through bridge tables or not at allgeeksforgeeks+1
What aspect of a star schema enables fast drill-down analysis in BI tools?
a. Use of unique indexes
b. Separate dimension tables for context
c. Normalized structure
d. Automatic clustering
Answer: b. Separate dimension tables for contextowox+2
Which of the following is NOT a strength of star schema design?
a. Query speed
b. Data simplicity
c. Strong normalization
d. Intuitive reporting
Answer: c. Strong normalizationthoughtspot+2
In AI applications, how do star schemas support feature selection?
a. By reducing metadata
b. By collecting descriptive data in dimensions for easy selection
c. By encrypting dimensions
d. By creating multiple fact tables
Answer: b. By collecting descriptive data in dimensions for easy selectiondatabricks+1
What is typically stored in the “Product” dimension table?
a. Sales Transaction IDs
b. Product attributes like category, brand, and description
c. Financial facts
d. Date values only
Answer: b. Product attributes like category, brand, and descriptionwikipedia+2
In a star schema, which operation is performed MOST quickly due to its design?
a. Large updates to all dimension data
b. Aggregation and filtering across dimensions
c. Deep hierarchical queries across dimensions
d. Real-time transactional writes
Answer: b. Aggregation and filtering across dimensionsmatillion+2
How does a star schema facilitate BI dashboard creation?
a. By storing data as blob objects
b. By providing a clear, business-centric data model
c. By splitting measures in multiple tables
d. By exclusively using primary keys
Answer: b. By providing a clear, business-centric data modelowox+2
Which statement about star schema storage is TRUE?
a. Always lowest storage footprint
b. Data typically duplicated (denormalized)
c. Data is never duplicated
d. Always demands cloud-only storage
Answer: b. Data typically duplicated (denormalized)builtin+2
What makes star schema tables easy for business analysts to understand?
a. Normalized entity-relationship diagrams
b. Intuitive links between facts and context-providing dimensions
c. Complex joins and nested keys
d. Free-text columns only
Answer: b. Intuitive links between facts and context-providing dimensionsowox+2
In analytics, what is the purpose of the “Customer” dimension in a star schema?
a. Store only monetary values
b. Describe customer demographics and segments
c. Record password data
d. Contain only numerical keys
Answer: b. Describe customer demographics and segmentsshieldbase+2
What is one limitation of changing dimension data in star schemas?
a. Updates cascade automatically
b. Hard to maintain history (slowly changing dimensions)
c. Change is reflected in all records instantly
d. All changes tracked via normalization
Answer: b. Hard to maintain history (slowly changing dimensions)matillion+2
Which is an ideal workload for a star schema?
a. OLTP systems
b. Historical trend analysis with large datasets
c. Frequent schema migrations
d. Document-based search
Answer: b. Historical trend analysis with large datasetsdatacamp+2
What defines the “grain” of the fact table in star schema?
a. Its dimension keys
b. Level of detail stored (lowest granularity)
c. Surrogate key format
d. Number of foreign keys
Answer: b. Level of detail stored (lowest granularity)wikipedia+1
Which naming convention is best for fact and dimension tables in a star schema?
a. Random auto-generated names
b. Business-identifiable descriptive names
c. Single-letter table names
d. Machine-generated codes
Answer: b. Business-identifiable descriptive namesowox+1
When might a star schema be replaced by a snowflake schema?
a. When stricter normalization and storage optimization is needed
b. When using graph databases
c. When few dimensions exist
d. Never
Answer: a. When stricter normalization and storage optimization is neededsimplilearn+2
What key challenge may occur with star schema scaling?
a. Handling large dimension tables with many attributes
b. Supporting only a single query type
c. Dimension hierarchies grow automatically
d. No performance drop-off
Answer: a. Handling large dimension tables with many attributeskyvosinsights+1
The “Sales” fact table typically includes which?
a. Only product names
b. Sales amount, date ID, customer ID, product ID
c. Customer addresses only
d. Employee biographical data
Answer: b. Sales amount, date ID, customer ID, product IDtechtarget+2
Which BI challenge can star schema address efficiently?
a. Daily operational processing
b. Multi-dimensional ad-hoc analysis and drill-down
c. Continuous data streaming
d. Streaming log data
Answer: b. Multi-dimensional ad-hoc analysis and drill-downairbyte+2
What is one legacy feature of star schemas still highly valued today?
a. Support for unstructured data storage
b. Simplicity and performance for analytics
c. Native support for JSON fields
d. Automated machine learning pipelines
Answer: b. Simplicity and performance for analyticsairbyte+2
Which is NOT generally a dimension in star schemas for analytics?
a. Time
b. Location
c. Customer
d. Numerical sales amount
Answer: d. Numerical sales amountshieldbase+2
In terms of AI data preparation, what does star schema facilitate most?
a. Easy mapping from business process to analytical dataset
b. Instant time series forecasting
c. End-to-end encryption by default
d. Random attribute selection
Answer: a. Easy mapping from business process to analytical datasetdatabricks+2
What is the overall design rule when modeling star schema for AI analytics?
a. Always split dimensions into as many tables as possible
b. Place business event facts at the center and contextual, descriptive dimensions around them
c. Store facts and dimensions together
d. Use only primary keys everywhere
Answer: b. Place business event facts at the center and contextual, descriptive dimensions around themwikipedia+2

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