60 real Lakehouse & Architecture questions from the Big Data 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 Lakehouse?
Junior
A.an architecture that adds data-warehouse features like ACID transactions and governance directly on low-cost data-lake storage
B.a layered design organizing data into bronze raw, silver cleaned, and gold curated tables for progressive refinement
C.a data-warehouse pattern for tracking changes to dimension attributes over time, such as keeping history with type-2 rows
D.a dimensional modeling pattern for tracking attribute history, with Type 1 overwriting and Type 2 adding new versioned rows
A.Medallion architecture — a lakehouse table continuously appended from a streaming source and incrementally processed, often the bronze layer of a pipeline
B.Medallion architecture — a self-contained, governed, and documented dataset owned by a domain team and treated as a first-class deliverable in data mesh
C.Medallion architecture — a design that processes everything as a stream, replaying the log for reprocessing instead of maintaining a separate batch layer
D.Medallion architecture — a layered design organizing data into bronze raw, silver cleaned, and gold curated tables for progressive refinement
8. Which term means: "a decentralized approach where domain teams own their data as products served through a self-serve platform with federated governance"?
A.Data mesh — a layered design organizing data into bronze raw, silver cleaned, and gold curated tables for progressive refinement
B.Data mesh — the pattern of capturing inserts, updates, and deletes from a source database's log and streaming them into a lakehouse for near-real-time replication
C.Data mesh — a precomputed, stored query result that is incrementally refreshed so downstream reads avoid recomputing expensive aggregations
D.Data mesh — a decentralized approach where domain teams own their data as products served through a self-serve platform with federated governance
11. Which term means: "a design that runs parallel batch and speed layers and merges their outputs at query time to balance accuracy with low latency"?
A.the pattern of capturing inserts, updates, and deletes from a source database's log and streaming them into a lakehouse for near-real-time replication
B.the tracked path of data from sources through transformations to outputs, used for impact analysis, debugging, and governance
C.a precomputed, stored query result that is incrementally refreshed so downstream reads avoid recomputing expensive aggregations
D.a design that processes everything as a stream, replaying the log for reprocessing instead of maintaining a separate batch layer
14. Which term means: "a design that processes everything as a stream, replaying the log for reprocessing instead of maintaining a separate batch layer"?
A.Kappa architecture — a design that processes everything as a stream, replaying the log for reprocessing instead of maintaining a separate batch layer
B.Kappa architecture — an architecture that adds data-warehouse features like ACID transactions and governance directly on low-cost data-lake storage
C.Kappa architecture — a design that runs parallel batch and speed layers and merges their outputs at query time to balance accuracy with low latency
D.Kappa architecture — a central repository that stores raw structured and unstructured data at scale on low-cost object storage
A.Slowly changing dimension — a decentralized approach where domain teams own their data as products served through a self-serve platform with federated governance
B.Slowly changing dimension — a design that runs parallel batch and speed layers and merges their outputs at query time to balance accuracy with low latency
C.Slowly changing dimension — a data-warehouse pattern for tracking changes to dimension attributes over time, such as keeping history with type-2 rows
D.Slowly changing dimension — a precomputed, stored query result that is incrementally refreshed so downstream reads avoid recomputing expensive aggregations
A.Write-audit-publish — a data-quality pattern that writes to a hidden branch, runs validations, then publishes only if checks pass
B.Write-audit-publish — a data-warehouse pattern for tracking changes to dimension attributes over time, such as keeping history with type-2 rows
C.Write-audit-publish — a central repository that stores raw structured and unstructured data at scale on low-cost object storage
D.Write-audit-publish — a design that processes everything as a stream, replaying the log for reprocessing instead of maintaining a separate batch layer
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