STAR SCHEMA — Learn free

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Questions 1–10 of 10

1. What structure defines a star schema?
Question 1A. A central fact table connecting directly to multiple surrounding dimension tablesB. Dimension tables normalised into related sub-dimensionsC. A bridge table unioning keys from many factsD. Hubs, links and satellitesE. A single flat table holding all measures and attributes
2. What is the primary goal of the star schema?
Question 2A. Eliminating all redundancy through normalisationB. Delivering a simple, high-performance, business-friendly structure for analyticsC. Capturing bi-temporal history of every attributeD. Supporting high-volume transactional updatesE. Enabling procedural navigation between records
3. Which measures might a star schema fact table capture in a loan approval environment?
Question 3A. Segment, IncomeBand and AgeGroupB. City and RegionC. LoanAmount, InterestRate, ApprovalCount or RepaymentAmountD. Day, Month, Quarter and YearE. Branch and ExperienceYears
4. Which attributes are listed for Dim_Officer in the star schema example?
Question 4A. Segment and IncomeBandB. City and RegionC. Day and MonthD. Branch and ExperienceYearsE. LoanAmount and ApprovalFlag
5. Why does the star schema's shallow join path improve performance?
Question 5A. Because every table is stored in memoryB. Because the fact table has no foreign keysC. Because dimensions are joined to each other in long chainsD. Because queries never need the fact tableE. Because each dimension connects directly to the fact table with no deep chains of joins between dimensions
6. Which question is given as an example that executives can answer with the Fact_LoanApproval star schema?
Question 6A. How many loans were approved by officers with more than 10 years of experience?B. What did we believe on 1 February was valid on 15 January?C. Which customers share the same contact details?D. Which events must be replayed to rebuild loan L001?E. Which fields should the mobile app request?
7. Why do dashboard refresh times remain fast in a star schema even at high data volumes?
Question 7A. Because the data is pre-aggregated into a bridge tableB. Because each query typically joins the fact table directly to the relevant dimensionsC. Because dimensions are normalised into sub-dimensionsD. Because the schema is stored as JSONE. Because the fact table is partitioned by customer
8. Which of the following is NOT listed as a strength of the star schema?
Question 8A. SimplicityB. Strong alignment with BI toolsC. Optimisation for transactional updatesD. Minimal join complexityE. Easy to understand visually and conceptually
9. What trade-off does the star schema intentionally accept to maintain simplicity?
Question 9A. Slower query performanceB. Loss of the fact table grainC. Inability to use BI toolsD. Redundancy in dimension tablesE. Deep join chains between dimensions
10. How is the star schema regarded within data warehousing practice?
Question 10A. As an obsolete pre-relational designB. As a niche pattern used only for fraud detectionC. As a normalised design for OLTP systemsD. As a cross-cutting principle rather than a schemaE. As the default dimensional design pattern for data warehouses and analytical data marts