SNOWFLAKE SCHEMA — Learn free

Self-check

Questions 1–10 of 10

1. What distinguishes a snowflake schema from a star schema?
Question 1A. One or more dimension tables are normalised into related sub-dimensionsB. The fact table is split into several smaller fact tablesC. A bridge table replaces the fact tableD. Dimensions are removed entirelyE. All tables are stored in 6NF
2. What is the primary goal of the snowflake schema?
Question 2A. Minimising the number of joins in every queryB. Improving data quality, storage efficiency and reusability, especially for large shared dimensionsC. Supporting real-time event streamingD. Providing a single entry point for cross-fact queriesE. Storing behaviour alongside data
3. In the snowflake example, into which components might Dim_Customer be split?
Question 3A. Fact, Bridge and DimensionB. Hub, Link and SatelliteC. Customer, Demographics, Employment and GeographyD. Anchor, Knot, Attribute and TieE. Day, Month, Quarter and Year
4. How does a snowflake schema typically come into being?
Question 4A. It is designed from scratch as a 3NF warehouseB. It is generated automatically by a BI toolC. It is derived from a Data VaultD. It begins as a classic star schema whose wide dimensions are normalised over time into sub-dimensionsE. It is built by denormalising an Inmon EDW
5. Why is normalising shared dimensions valuable when multiple fact tables such as Loans, Collections and Marketing Campaigns exist?
Question 5A. It removes the need for a fact tableB. It makes every query a single-table readC. It allows the fact tables to be mergedD. It eliminates the need for a Date dimensionE. It ensures consistency of conformed dimensions across analytical domains
6. Why are snowflake schemas often paired with a semantic layer or BI modelling tool?
Question 6A. To present flattened, business-friendly views that hide the additional joins from analystsB. To convert the schema back into a star schema physicallyC. To enforce Third Normal Form on the fact tableD. To replay events into the dimensionsE. To store the dimensions as JSON documents
7. Which of the following is NOT listed as an advantage of the snowflake schema?
Question 7A. Reduced duplicationB. Fewer joins than a star schemaC. Improved storage efficiencyD. Clearer conformed hierarchiesE. Easier management of slowly changing attributes
8. In which situation is the snowflake schema particularly useful?
Question 8A. When dimensions are tiny and rarely changeB. When only one fact table existsC. When dimensions are large, frequently updated and shared across many fact tablesD. When transactional updates must be fastE. When queries must avoid all joins
9. Toward which normal-form principles does the snowflake schema move by partially normalising dimensions?
Question 9A. 5NF or 6NFB. DKNFC. 1NF onlyD. 2NF or 3NFE. BCNF strictly
10. Which query is given as an example that analysts can still run in the snowflake loan approval environment?
Question 10A. Which officers are linked to fraud rings?B. Which loan documents embed customer C101?C. What rate was recorded on 20 January?D. Which keys are in the Puppini Bridge?E. Approval rates by income band and region