Exercises
Assess your ability to design dimensional models for business intelligence systems. This quiz covers fact table grain, additive measures, surrogate keys, slowly changing dimensions, role-playing dimensions, bridge tables, factless facts, snapshot models, and other practical warehouse design patterns. Questions range from foundational concepts to challenging modeling scenarios.
Answer the questions below and check the explanation for each answer.
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The grain states exactly what one fact-table row represents, such as one product sold on one transaction line. It should be declared before selecting dimensions and measures.
Dimensions contain descriptive business context, such as product names, customer segments, and regions. Users employ these attributes to filter, group, and interpret measures.
A balance is semi-additive because it can be aggregated across entities such as customers, but summing it across time would usually produce a meaningless result.
A central fact table connected directly to denormalized dimension tables forms a star schema. This structure is generally simple for BI users and analytical queries.
Surrogate keys are warehouse-controlled identifiers. They isolate the model from source-key changes and support multiple historical versions of the same business entity.
Type 2 preserves history by creating a new row with a new surrogate key when tracked attributes change. Effective dates or current-row indicators identify each version.
Type 1 updates overwrite existing values. This is appropriate for corrections or attributes whose historical values do not need to be retained.
A role-playing dimension is reused for different business roles. Here, the same Date dimension describes both the order date and the shipment date.
A degenerate dimension is a business identifier, such as an invoice or order number, stored directly in the fact table without a corresponding dimension table.
A factless fact table records that an event occurred or that a relationship existed without storing numeric measures. Each row here records student attendance.
A conformed dimension is consistently defined and shared across subject areas. It enables comparable analysis, such as reporting sales and inventory by the same product categories.
A bridge table resolves a many-to-many relationship by storing one row for each valid representative-team association. It may also contain allocation weights.
An accumulating snapshot tracks a process with defined milestones. The same row is updated as each stage is completed, enabling analysis of elapsed time between stages.
A periodic snapshot records measurements at regular intervals, such as daily inventory levels. Rows represent the state at each scheduled reporting period rather than individual movements.
An inferred member provides a temporary dimension row and surrogate key, allowing the fact to load without losing the event. Its attributes are completed when source details arrive.
A junk dimension combines miscellaneous flags and low-cardinality indicators into one dimension. This avoids placing numerous small dimension keys or descriptive flags in the fact table.
Allocation weights distribute a measure across multiple memberships. If the weights for each customer total 1, weighted aggregation avoids duplicating the full revenue in every segment.
Normalizing dimension hierarchies can reduce redundant values, but it creates a snowflake-like structure with additional joins. This may make BI models less intuitive and queries more complex.
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