MCQ Collection
Data Warehousing and Business Intelligence MCQs
Practice Data Warehousing and Business Intelligence questions with answers and explanations.
Choose an option to check your answer.
A.
Normalize every dimension into many tables
B.
Remove surrogate keys from facts
C.
Efficiently filter a large fact table using selective dimension predicates
D.
Force all joins to use nested loops
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Correct Answer: C. Efficiently filter a large fact table using selective dimension predicates
Explanation:
Star queries combine a very large fact table with smaller dimensions.
Choose an option to check your answer.
A.
By each dashboard user independently
B.
Inside every source application using different rules
C.
Within the controlled warehouse loading process
D.
From the current fact measure value
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Correct Answer: C. Within the controlled warehouse loading process
Explanation:
Warehouse-controlled generation keeps keys unique and independent of source systems.
Choose an option to check your answer.
A.
Insert rows one at a time through a dashboard
B.
Recalculate every report after each row
C.
Use set-based bulk loading with appropriate minimal logging and index strategy
D.
Create many unnecessary indexes before the load
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Correct Answer: C. Use set-based bulk loading with appropriate minimal logging and index strategy
Explanation:
Bulk, set-based operations reduce per-row overhead and exploit database throughput.
Choose an option to check your answer.
A.
Update only changed dimension members
B.
Refresh report colors without reading data
C.
Reload all relevant data and rebuild dependent structures
D.
Execute one user query and discard the result
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Correct Answer: C. Reload all relevant data and rebuild dependent structures
Explanation:
Full processing reconstructs the model from its source data.
Choose an option to check your answer.
A.
It treats every NULL as zero automatically
B.
It raises an error whenever a NULL exists
C.
It excludes NULL values from both the numerator and count
D.
It converts NULL to the group maximum
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Correct Answer: C. It excludes NULL values from both the numerator and count
Explanation:
AVG computes the sum of non-NULL values divided by their non-NULL count.
Choose an option to check your answer.
A.
Sales amount
B.
Units sold
C.
Discount value
D.
Product brand
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Correct Answer: D. Product brand
Explanation:
Dimension attributes describe entities and are used for labels, filters, and grouping.
Choose an option to check your answer.
A.
Scanning billions of rows for grouped totals
B.
Compressing repeated values in a large fact column
C.
Batch-mode aggregation across many segments
D.
Highly selective point lookups and small-row transactional access
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Correct Answer: D. Highly selective point lookups and small-row transactional access
Explanation:
Rowstore indexes efficiently navigate to a small number of matching rows.
Choose an option to check your answer.
A.
To capture each source transaction before validation
B.
To store user passwords separately
C.
To replace all detailed facts permanently
D.
To pre-summarize data for frequently requested query levels
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Correct Answer: D. To pre-summarize data for frequently requested query levels
Explanation:
Aggregate tables reduce the number of detailed rows processed for common summaries.
Choose an option to check your answer.
A.
Overwrite the existing row without history
B.
Delete all facts referencing the old version
C.
Reuse the same row and remove effective dates
D.
Expire the old version and insert a new row with a new surrogate key
Show Answer
Correct Answer: D. Expire the old version and insert a new row with a new surrogate key
Explanation:
Type 2 preserves both old and new attribute states as separate dimension rows.
Choose an option to check your answer.
A.
Designing visual themes for reports
B.
Normalizing every dimension table
C.
Replacing all data-quality checks with alerts
D.
Coordinating task order, schedules, dependencies, retries, and monitoring across a pipeline
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Correct Answer: D. Coordinating task order, schedules, dependencies, retries, and monitoring across a pipeline
Explanation:
Orchestration controls how multiple data tasks run as a reliable workflow.
Choose an option to check your answer.
A.
When the entire cube storage must be deleted and rebuilt
B.
When no source data has changed
C.
When a dashboard needs a different font
D.
When dimension members or attributes changed and existing data should be retained where possible
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Correct Answer: D. When dimension members or attributes changed and existing data should be retained where possible
Explanation:
Process Update incorporates dimension changes without always performing a complete rebuild.
Choose an option to check your answer.
A.
Type 2 historical rows
B.
Type 1 overwrite
C.
Type 0 fixed attribute
D.
Deleting the customer dimension row
Show Answer
Correct Answer: A. Type 2 historical rows
Explanation:
Type 2 creates a new dimension row with a new surrogate key for each version.
