dbt interview questions

1. A.the dbt macro guarding the filter that selects only newer rows
2. 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.
3. C.A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
4. D.a model rebuilt as a database view

Answer + AI explanation with Pro

1. A.singular vs generic test
2. B.unit test
3. C.dbt view materialization
4. D.dbt test

Answer + AI explanation with Pro

1. A.dbt view materialization — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
2. 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.
3. C.dbt view materialization — the dbt macro guarding the filter that selects only newer rows
4. D.dbt view materialization — a model rebuilt as a database view

Answer + AI explanation with Pro

1. A.dbt's implementation of SCD Type 2 history tracking
2. B.A dbt materialization that is not built as a database object but inlined as a CTE into downstream models, useful for lightweight intermediate logic.
3. C.a model that only processes new/changed rows
4. D.A dbt-managed CSV file loaded into the warehouse as a table, used for small static lookup or reference data versioned in the repo.

Answer + AI explanation with Pro

1. A.ephemeral model
2. B.dbt view materialization
3. C.ref()
4. D.dbt incremental materialization

Answer + AI explanation with Pro

1. A.dbt incremental materialization — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
2. 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.
3. 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.
4. D.dbt incremental materialization — a model that only processes new/changed rows

Answer + AI explanation with Pro

1. A.The June 2025 dbt release on the 1.x line that advanced unit tests, microbatch incremental models, model contracts, and sample-mode features.
2. B.the dbt function that builds the model dependency graph and lineage
3. C.the dbt macro guarding the filter that selects only newer rows
4. D.A dbt materialization that is not built as a database object but inlined as a CTE into downstream models, useful for lightweight intermediate logic.

Answer + AI explanation with Pro

1. A.is_incremental()
2. B.model contract
3. C.dbt view materialization
4. D.model version

Answer + AI explanation with Pro

1. A.is_incremental() — the dbt macro guarding the filter that selects only newer rows
2. B.is_incremental() — A dbt check (dbt source freshness) comparing a source table's max loaded_at to thresholds to confirm upstream data is current.
3. C.is_incremental() — an assertion of data quality (unique, not_null, accepted_values, relationships)
4. D.is_incremental() — the dbt function that builds the model dependency graph and lineage

Answer + AI explanation with Pro

1. A.a model rebuilt as a database view
2. 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.
3. C.dbt's implementation of SCD Type 2 history tracking
4. D.the dbt macro guarding the filter that selects only newer rows

Answer + AI explanation with Pro

1. A.dbt incremental materialization
2. B.dbt snapshot
3. C.source freshness
4. D.dbt view materialization

Answer + AI explanation with Pro

1. 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.
2. B.dbt snapshot — dbt's implementation of SCD Type 2 history tracking
3. C.dbt snapshot — the dbt macro guarding the filter that selects only newer rows
4. D.dbt snapshot — a model that only processes new/changed rows

Answer + AI explanation with Pro

1. A.an assertion of data quality (unique, not_null, accepted_values, relationships)
2. B.A dbt incremental strategy that processes data in time-based batches (event_time and batch_size), simplifying large backfills and reprocessing.
3. C.a model that only processes new/changed rows
4. D.A dbt check (dbt source freshness) comparing a source table's max loaded_at to thresholds to confirm upstream data is current.

Answer + AI explanation with Pro

1. A.dbt snapshot
2. B.singular vs generic test
3. C.dbt test
4. D.macro

Answer + AI explanation with Pro

1. A.dbt test — dbt's implementation of SCD Type 2 history tracking
2. B.dbt test — an assertion of data quality (unique, not_null, accepted_values, relationships)
3. 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.
4. 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.

Answer + AI explanation with Pro

1. A.A dbt resource documenting a downstream consumer (dashboard, app, ML model) of dbt models, surfacing lineage and tying tests to deliverables.
2. 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.
3. C.the dbt function that builds the model dependency graph and lineage
4. D.an assertion of data quality (unique, not_null, accepted_values, relationships)

Answer + AI explanation with Pro

1. A.ref()
2. B.is_incremental()
3. C.ephemeral model
4. D.dbt snapshot

Answer + AI explanation with Pro

1. A.ref() — the dbt function that builds the model dependency graph and lineage
2. 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.
3. C.ref() — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
4. D.ref() — a model that only processes new/changed rows

Answer + AI explanation with Pro

1. A.A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
2. B.The June 2025 dbt release on the 1.x line that advanced unit tests, microbatch incremental models, model contracts, and sample-mode features.
3. C.the dbt macro guarding the filter that selects only newer rows
4. D.an assertion of data quality (unique, not_null, accepted_values, relationships)

Answer + AI explanation with Pro

1. A.dbt view materialization
2. B.dbt incremental materialization
3. C.seed
4. D.dbt 1.10

Answer + AI explanation with Pro

1. A.dbt 1.10 — A reusable Jinja function in dbt that templates SQL, reducing repetition and enabling parameterized transformations.
2. B.dbt 1.10 — A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
3. 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.
4. 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.

Answer + AI explanation with Pro

1. A.a model rebuilt as a database view
2. B.an assertion of data quality (unique, not_null, accepted_values, relationships)
3. C.the dbt macro guarding the filter that selects only newer rows
4. 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.

Answer + AI explanation with Pro

1. A.dbt 1.10
2. B.dbt incremental materialization
3. C.unit test
4. D.dbt test

Answer + AI explanation with Pro

1. 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.
2. B.unit test — A dbt resource documenting a downstream consumer (dashboard, app, ML model) of dbt models, surfacing lineage and tying tests to deliverables.
3. 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.
4. D.unit test — the dbt macro guarding the filter that selects only newer rows

Answer + AI explanation with Pro

1. 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.
2. B.A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
3. C.A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
4. 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.

Answer + AI explanation with Pro

1. A.dbt test
2. B.is_incremental()
3. C.seed
4. D.model contract

Answer + AI explanation with Pro

1. A.model contract — a model rebuilt as a database view
2. B.model contract — A dbt check (dbt source freshness) comparing a source table's max loaded_at to thresholds to confirm upstream data is current.
3. C.model contract — A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
4. D.model contract — the dbt macro guarding the filter that selects only newer rows

Answer + AI explanation with Pro

1. A.A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
2. B.dbt's implementation of SCD Type 2 history tracking
3. C.the dbt macro guarding the filter that selects only newer rows
4. D.A dbt-managed CSV file loaded into the warehouse as a table, used for small static lookup or reference data versioned in the repo.

Answer + AI explanation with Pro

1. A.is_incremental()
2. B.singular vs generic test
3. C.model version
4. D.model contract

Answer + AI explanation with Pro

1. 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.
2. B.model version — A dbt feature letting one model expose multiple versioned definitions so consumers can migrate gradually between breaking changes.
3. C.model version — A dbt configuration enforcing a model's column names, data types, and constraints at build time so schema changes fail fast.
4. D.model version — an assertion of data quality (unique, not_null, accepted_values, relationships)

Answer + AI explanation with Pro