DBT Deployment & CI/CD Flashcards

What is the purpose of dbt Cloud's Slim CI feature?

Answer: Runs only models that have changed since the last successful job

Slim CI uses the `state:modified` selector with a deferred environment to run only the models impacted by a pull request, saving time and cost.

Which selector is used in dbt to run only models that differ from a previous state manifest?

Answer: --select state:modified

`state:modified` compares the project against a baseline manifest.json to identify nodes with code or config changes.

What environment variable or flag tells dbt where to find the baseline manifest for state comparison?

Answer: --state or DBT_STATE

The `--state` flag (or `DBT_STATE` env var) specifies the directory containing the `manifest.json` to use as the comparison baseline.

In a dbt Cloud job, what does enabling 'Generate docs' do?

Answer: Runs `dbt docs generate` to refresh the catalog artifact after the run

The 'Generate docs' toggle appends a `dbt docs generate` step to the job, refreshing the catalog.json and making docs available in dbt Cloud.

Which dbt Core command compiles SQL without executing it against the warehouse?

Answer: dbt compile

`dbt compile` resolves all Jinja and ref/source calls and writes the compiled SQL to the `target/compiled/` directory without running queries.

What is the recommended way to handle secrets (e.g., database passwords) in a dbt CI pipeline?

Answer: Use environment variables injected by the CI platform

Secrets should be passed as environment variables from the CI platform (GitHub Actions, GitLab CI, etc.) and referenced in profiles.yml using `env_var()`.