51 real dbt 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 dbt view materialization?
Junior
A.the dbt macro guarding the filter that selects only newer rows
B.A dbt-managed CSV file loaded into the warehouse as a table, used for small static lookup or reference data versioned in the repo.
C.A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
A.dbt view materialization — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
B.dbt view materialization — A dbt resource documenting a downstream consumer (dashboard, app, ML model) of dbt models, surfacing lineage and tying tests to deliverables.
C.dbt view materialization — the dbt macro guarding the filter that selects only newer rows
D.dbt view materialization — a model rebuilt as a database view
A.dbt incremental materialization — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
B.dbt incremental materialization — The June 2025 dbt release on the 1.x line that advanced unit tests, microbatch incremental models, model contracts, and sample-mode features.
C.dbt incremental materialization — A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
D.dbt incremental materialization — a model that only processes new/changed rows
B.A dbt test that supplies mocked input rows and asserts expected output of a model's SQL logic, validating transformations without touching warehouse data.
C.dbt's implementation of SCD Type 2 history tracking
D.the dbt macro guarding the filter that selects only newer rows
A.dbt snapshot — A dbt materialization that is not built as a database object but inlined as a CTE into downstream models, useful for lightweight intermediate logic.
B.dbt snapshot — dbt's implementation of SCD Type 2 history tracking
C.dbt snapshot — the dbt macro guarding the filter that selects only newer rows
D.dbt snapshot — a model that only processes new/changed rows
A.dbt test — dbt's implementation of SCD Type 2 history tracking
B.dbt test — an assertion of data quality (unique, not_null, accepted_values, relationships)
C.dbt test — A dbt test that supplies mocked input rows and asserts expected output of a model's SQL logic, validating transformations without touching warehouse data.
D.dbt test — A dbt materialization that is not built as a database object but inlined as a CTE into downstream models, useful for lightweight intermediate logic.
A.A dbt resource documenting a downstream consumer (dashboard, app, ML model) of dbt models, surfacing lineage and tying tests to deliverables.
B.A singular dbt test is a one-off SQL query returning failing rows, while a generic test is a reusable parameterized test (like not_null) applied via YAML.
C.the dbt function that builds the model dependency graph and lineage
D.an assertion of data quality (unique, not_null, accepted_values, relationships)
A.ref() — the dbt function that builds the model dependency graph and lineage
B.ref() — A dbt materialization that is not built as a database object but inlined as a CTE into downstream models, useful for lightweight intermediate logic.
C.ref() — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
D.ref() — a model that only processes new/changed rows
20. Which term means: "The June 2025 dbt release on the 1.x line that advanced unit tests, microbatch incremental models, model contracts, and sample-mode features."?
A.dbt 1.10 — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
B.dbt 1.10 — A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
C.dbt 1.10 — A dbt incremental strategy that processes data in time-based batches (event_time and batch_size), simplifying large backfills and reprocessing.
D.dbt 1.10 — The June 2025 dbt release on the 1.x line that advanced unit tests, microbatch incremental models, model contracts, and sample-mode features.
B.an assertion of data quality (unique, not_null, accepted_values, relationships)
C.the dbt macro guarding the filter that selects only newer rows
D.A dbt test that supplies mocked input rows and asserts expected output of a model's SQL logic, validating transformations without touching warehouse data.
23. Which term means: "A dbt test that supplies mocked input rows and asserts expected output of a model's SQL logic, validating transformations without touching warehouse data."?
A.unit test — A dbt test that supplies mocked input rows and asserts expected output of a model's SQL logic, validating transformations without touching warehouse data.
B.unit test — A dbt resource documenting a downstream consumer (dashboard, app, ML model) of dbt models, surfacing lineage and tying tests to deliverables.
C.unit test — The June 2025 dbt release on the 1.x line that advanced unit tests, microbatch incremental models, model contracts, and sample-mode features.
D.unit test — the dbt macro guarding the filter that selects only newer rows
A.A dbt test that supplies mocked input rows and asserts expected output of a model's SQL logic, validating transformations without touching warehouse data.
B.A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
C.A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
D.A singular dbt test is a one-off SQL query returning failing rows, while a generic test is a reusable parameterized test (like not_null) applied via YAML.
29. Which term means: "A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes."?
A.model version — A dbt materialization that is not built as a database object but inlined as a CTE into downstream models, useful for lightweight intermediate logic.
B.model version — A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
C.model version — A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
D.model version — an assertion of data quality (unique, not_null, accepted_values, relationships)
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