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dbt Interview Questions

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1

What is dbt and where does it fit in the data stack? Normal

dbt (data build tool) is used for the “T” in ELT: it runs SQL transformations inside your data warehouse. You write SQL in models, use ref() to build a DAG of dependencies, and dbt handles run order, testing, and documentation. It doesn’t move data from source to warehouse—an orchestrator (e.g. Airflow) does that. dbt runs in the warehouse (Snowflake, BigQuery, Redshift, etc.) and produces tables/views that analytics and BI use.

2

What are the main materialization types in dbt? Normal

View: Model is built as a view (no physical table; always fresh, can be slower). Table: Full rebuild each run; good for smaller datasets. Incremental: Only new/changed rows are processed after the first run; good for large fact tables. Ephemeral: Not materialized in the warehouse; compiled into a CTE in dependent models. Choose by data volume and how often you need to refresh.

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25+ questions across models, testing, incremental, macros, and project design — with full answers.

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Models & refs
3

What is ref() and why use it instead of raw table names? Normal

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4

Write a staging model that selects from source and renames columns. Code

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5

Staging vs intermediate vs marts — how do you structure a dbt project? Logic

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Testing & documentation
6

How does dbt testing work (schema tests vs custom tests)? Normal

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7

Add unique and not_null tests to a model in YAML. Code

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8

When would you use a singular test vs a generic test? Logic

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Incremental & performance
9

How do incremental models work? What is the incremental_strategy? Normal

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10

Write an incremental model that appends new rows based on a date column. Code

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11

How would you implement SCD Type 2 in dbt? Logic

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Macros, packages & deployment
12

What are dbt macros and when do you use them? Normal

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13

How do you run only a subset of models (e.g. select or exclude)? Code

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14

How would you deploy dbt in CI/CD with Slim CI? Logic

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