Data Modeling interview questions

1. A.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
2. B.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
3. C.In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
4. D.A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.

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1. A.conformed dimension
2. B.factless fact table
3. C.Inmon approach
4. D.junk dimension

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1. A.conformed dimension — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
2. B.conformed dimension — A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
3. C.conformed dimension — In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.
4. D.conformed dimension — A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.

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1. A.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
2. B.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
3. C.A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
4. D.A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.

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1. A.SCD Type 2
2. B.degenerate dimension
3. C.link table
4. D.Data Vault

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1. A.degenerate dimension — In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.
2. B.degenerate dimension — A table resolving a many-to-many relationship between a fact and a dimension, often carrying an allocation weight to avoid double counting.
3. C.degenerate dimension — A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
4. D.degenerate dimension — A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.

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1. A.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
2. B.A table resolving a many-to-many relationship between a fact and a dimension, often carrying an allocation weight to avoid double counting.
3. C.A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
4. D.A table resolving many-to-many relationships between a fact and a dimension, often carrying an allocation or weighting factor.

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1. A.junk dimension
2. B.fan trap
3. C.conformed dimension
4. D.Data Vault 2.0

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1. A.junk dimension — A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
2. B.junk dimension — In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
3. C.junk dimension — A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
4. D.junk dimension — In Data Vault, a table modeling a many-to-many relationship or transaction between two or more hubs by their business keys.

Answer + AI explanation with Pro

1. A.A modeling pattern using hubs (keys), links (relationships), and satellites (descriptive history) to build an auditable, scalable warehouse.
2. B.A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
3. C.A fact table that records the occurrence of events or coverage with no numeric measures, such as student attendance or product-promotion eligibility.
4. D.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.

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1. A.SCD Type 2
2. B.factless fact table
3. C.Data Vault 2.0
4. D.junk dimension

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1. A.factless fact table — A fact table that records the occurrence of events or coverage with no numeric measures, such as student attendance or product-promotion eligibility.
2. B.factless fact table — A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
3. C.factless fact table — A table resolving a many-to-many relationship between a fact and a dimension, often carrying an allocation weight to avoid double counting.
4. D.factless fact table — A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.

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1. A.A slowly changing dimension technique that preserves history by inserting a new row per change with effective-from, effective-to, and current flags.
2. B.A modeling pitfall where a one-to-many join causes additive measures to be double-counted, avoided by aggregating at the correct grain first.
3. C.A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
4. D.Explicitly stating what one row of a fact table represents (the lowest level of detail), the first and most important step in dimensional design.

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1. A.SCD Type 2
2. B.Data Vault 2.0
3. C.link table
4. D.hub

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1. A.Data Vault 2.0 — A modeling pattern using hubs (keys), links (relationships), and satellites (descriptive history) to build an auditable, scalable warehouse.
2. B.Data Vault 2.0 — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
3. C.Data Vault 2.0 — A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
4. D.Data Vault 2.0 — A modeling pitfall where a one-to-many join causes additive measures to be double-counted, avoided by aggregating at the correct grain first.

Answer + AI explanation with Pro

1. A.A fact table that records the occurrence of events or coverage with no numeric measures, such as student attendance or product-promotion eligibility.
2. B.A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
3. C.In Data Vault, a table modeling a many-to-many relationship or transaction between two or more hubs by their business keys.
4. D.In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.

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1. A.Kimball approach
2. B.Data Vault 2.0
3. C.grain declaration
4. D.hub

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1. A.hub — A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
2. B.hub — In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.
3. C.hub — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
4. D.hub — In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.

Answer + AI explanation with Pro

1. A.A slowly changing dimension technique that preserves history by inserting a new row per change with effective-from, effective-to, and current flags.
2. B.In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
3. C.A table resolving a many-to-many relationship between a fact and a dimension, often carrying an allocation weight to avoid double counting.
4. D.In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.

Answer + AI explanation with Pro

1. A.link table
2. B.satellite
3. C.Data Vault 2.0
4. D.Kimball approach

Answer + AI explanation with Pro

1. A.satellite — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
2. B.satellite — In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
3. C.satellite — In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.
4. D.satellite — In Data Vault, a table modeling a many-to-many relationship or transaction between two or more hubs by their business keys.

Answer + AI explanation with Pro

1. A.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
2. B.A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
3. C.In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
4. D.In Data Vault, a table modeling a many-to-many relationship or transaction between two or more hubs by their business keys.

Answer + AI explanation with Pro

1. A.satellite
2. B.link table
3. C.SCD Type 2
4. D.Kimball approach

Answer + AI explanation with Pro

1. A.link table — In Data Vault, a table modeling a many-to-many relationship or transaction between two or more hubs by their business keys.
2. B.link table — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
3. C.link table — A table resolving many-to-many relationships between a fact and a dimension, often carrying an allocation or weighting factor.
4. D.link table — A modeling pitfall where a one-to-many join causes additive measures to be double-counted, avoided by aggregating at the correct grain first.

Answer + AI explanation with Pro

1. A.A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
2. B.A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.
3. C.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
4. D.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.

Answer + AI explanation with Pro

1. A.bridge table
2. B.Data Vault 2.0
3. C.Inmon approach
4. D.hub

Answer + AI explanation with Pro

1. A.Inmon approach — A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.
2. B.Inmon approach — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
3. C.Inmon approach — A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
4. D.Inmon approach — A modeling pitfall where a one-to-many join causes additive measures to be double-counted, avoided by aggregating at the correct grain first.

Answer + AI explanation with Pro

1. A.A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
2. B.A modeling pitfall where a one-to-many join causes additive measures to be double-counted, avoided by aggregating at the correct grain first.
3. C.A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.
4. D.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.

Answer + AI explanation with Pro

1. A.Data Vault 2.0
2. B.Kimball approach
3. C.fan trap
4. D.factless fact table

Answer + AI explanation with Pro

1. A.Kimball approach — A slowly changing dimension technique that preserves history by inserting a new row per change with effective-from, effective-to, and current flags.
2. B.Kimball approach — A table resolving a many-to-many relationship between a fact and a dimension, often carrying an allocation weight to avoid double counting.
3. C.Kimball approach — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
4. D.Kimball approach — A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.

Answer + AI explanation with Pro