What each core dbt command actually does
dbt provides a set of core commands that run, test, document, and inspect your project. Understanding when to use each command—and how they interact through selection syntax and the DAG—is essential for efficient development and CI workflows. dbt build is the most commonly tested command because it consolidates multiple steps into one graph-aware operation.
Must-know
dbt runexecutes models only (materializes them as tables/views), whiledbt buildruns models, tests, snapshots, and seeds together in DAG order, stopping downstream nodes if an upstream test fails.dbt testexecutes schema tests (e.g., unique,not_null) and singular tests defined in thetests/data-testsfolder, but does not build or refresh the underlying models first.dbt seedloads CSV files from theseedsdirectory into the warehouse as tables, useful for static reference or lookup data, not for large or frequently changing datasets.dbt snapshotcaptures point-in-time changes to mutable source tables using a defined strategy (timestamp or check), enabling type-2 slowly changing dimension tracking.dbt docs generatecompiles project metadata (including compiled SQL, column-level descriptions, and lineage) into a manifest and catalog, whichdbt docs servethen renders as a browsable website.dbt showcompiles and previews the result set of a model or ad hoc SQL without materializing it in the warehouse, useful for quick debugging during development.- Selection flags like
--select,--exclude, and graph operators (e.g.,+model_name,model_name+) work acrossrun,test,build, andsnapshotto scope execution to specific parts of the DAG.
You maintain this snapshot:
{% snapshot orders_snapshot %}
{{
config(
target_schema='snapshots',
unique_key='order_id',
strategy='timestamp',
updated_at='updated_at',
)
}}
select * from {{ source('erp', 'orders') }}
{% endsnapshot %}A backend script updates the status column on some rows directly in the database without ever touching updated_at. Snapshot runs since then have not recorded those status changes. Which change to the config fixes this?
What you have tried across dbt Analytics Engineering's objectives, not a readiness score.
Developing and optimizing dbt models
- Tracing and confirming a model's upstream raw sources
- How dbt's core materialization types differ
- Keeping models modular and avoiding repeated logic
- What each core dbt command actually does
- Shaping a model's dependency graph so it stays readable
- Setting project-wide configuration in dbt_project.yml
- Pulling in and using a dbt package
- Writing a model in Python instead of SQL
- Granting model access through the grants config
- Configuring a snapshot to track slowly changing data
- Picking the right incremental strategy for a dataset
- Dry-running a model to check its logic and schema before a real build
- Sampling a model run before a full build
- What microbatch and other advanced materializations are for
Managing dbt models governance
Debugging data modeling errors
Troubleshooting and optimizing dbt pipelines
Implementing dbt tests
Implementing and maintaining external dependencies
Coverage checked against the published exam guide on Aug 5, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.