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Data Warehouse Modeling Flashcards

7 cards from real Data Engineering practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Data Warehouse Modeling flashcards as text

  1. An aggregate (or summary) fact table is built mainly to:

    Answer: Improve query performance by pre-summarizing data at a coarser grain

    Aggregate fact tables pre-compute roll-ups to speed up common summary queries.

  2. Additive measures in a fact table are those that:

    Answer: Can be summed across all dimensions

    Additive measures, like sales amount, can be summed meaningfully across every dimension.

  3. A measure like 'account balance' that cannot be summed across the time dimension is called:

    Answer: Semi-additive

    Semi-additive measures can be summed across some dimensions but not time, such as balances or inventory levels.

  4. A ratio or percentage stored as a measure is typically:

    Answer: Non-additive

    Ratios and percentages are non-additive and should be recomputed from their additive components rather than summed.

  5. Why is a dedicated Date dimension preferred over storing a raw date column in the fact table?

    Answer: It enables rich attributes like fiscal period, weekday, and holiday flags for filtering

    A Date dimension provides descriptive calendar attributes that support flexible, business-friendly time analysis.

  6. What does denormalizing a dimension table generally improve?

    Answer: Query simplicity and read performance by reducing joins

    Denormalized (flat) dimensions reduce the number of joins, simplifying and speeding up analytical queries.

  7. In a galaxy (fact constellation) schema, what is shared across multiple fact tables?

    Answer: Conformed dimension tables

    A galaxy schema links multiple fact tables through shared conformed dimensions.