51 real Data Modeling questions from the Data Engineering bank, as asked in Indian campus drives and tech interviews. Every question has a verified answer and an AI-tutor explanation on placd — free to start.
1. What is conformed dimension?
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A.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
B.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
C.In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
D.A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
2. Which term means: "A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture."?
A.conformed dimension — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
B.conformed dimension — A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
C.conformed dimension — In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.
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.
A.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
B.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
C.A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
D.A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.
A.degenerate dimension — In Data Vault, a table storing the time-variant descriptive attributes and history attached to a hub or link.
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.
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.
D.degenerate dimension — A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
A.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
B.A table resolving a many-to-many relationship between a fact and a dimension, often carrying an allocation weight to avoid double counting.
C.A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
D.A table resolving many-to-many relationships between a fact and a dimension, often carrying an allocation or weighting factor.
8. Which term means: "A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns."?
A.junk dimension — A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
B.junk dimension — In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
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.
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.
A.A modeling pattern using hubs (keys), links (relationships), and satellites (descriptive history) to build an auditable, scalable warehouse.
B.A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
C.A fact table that records the occurrence of events or coverage with no numeric measures, such as student attendance or product-promotion eligibility.
D.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
11. Which term means: "A fact table that records the occurrence of events or coverage with no numeric measures, such as student attendance or product-promotion eligibility."?
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.
B.factless fact table — A dimension attribute (like an order or invoice number) stored in the fact table itself with no corresponding dimension table.
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.
D.factless fact table — A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
14. Which term means: "A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses."?
A.Data Vault 2.0 — A modeling pattern using hubs (keys), links (relationships), and satellites (descriptive history) to build an auditable, scalable warehouse.
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.
C.Data Vault 2.0 — A modeling methodology using hubs (business keys), links (relationships), and satellites (descriptive history) for auditable, agile, highly scalable warehouses.
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.
A.A fact table that records the occurrence of events or coverage with no numeric measures, such as student attendance or product-promotion eligibility.
B.A dimension that consolidates several low-cardinality flags and indicators into one table to avoid cluttering the fact table with many small columns.
C.In Data Vault, a table modeling a many-to-many relationship or transaction between two or more hubs by their business keys.
D.In Data Vault, a table holding the unique list of business keys for an entity plus metadata, with no descriptive attributes.
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.
B.link table — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
C.link table — A table resolving many-to-many relationships between a fact and a dimension, often carrying an allocation or weighting factor.
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.
A.A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
B.A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.
C.A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
D.A dimension shared and used identically across multiple fact tables or data marts, enabling consistent cross-process reporting in a Kimball bus architecture.
26. Which term means: "A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it."?
A.Inmon approach — A top-down methodology building a normalized (3NF) enterprise data warehouse first, then deriving dimensional data marts from it.
B.Inmon approach — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
C.Inmon approach — A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
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.
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.
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.
C.Kimball approach — A modeling pitfall where joining two unrelated many-to-many paths through a shared dimension produces a Cartesian explosion of rows.
D.Kimball approach — A bottom-up methodology building conformed dimensional data marts (star schemas) tied together by a shared bus matrix.
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