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Data Engineering Path  ·  Airflow
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Theoretical Quiz — Module 9

Test Your Knowledge

Q1. What does Cosmos actually generate from a dbt project, and where does it get the task dependencies from?

Show Answer

One Airflow task per dbt model. Dependencies come directly from the dbt project's own ref() calls between models (parsed from the project's manifest) - not hand-wired in the DAG code.

Q2. Why does Kafka rarely appear as a task running inside an Airflow DAG, doing continuous stream processing?

Show Answer

Airflow is a batch orchestrator, not a streaming platform - a task is expected to start, do bounded work, and finish. Kafka usually appears at the edges instead: publishing a "finished" event, or consuming a bounded batch as one discrete task.

Q3. With a Secrets Backend configured, does Airflow check the backend or its own metadata database first when resolving a Connection?

Show Answer

The Secrets Backend is checked first. If it doesn't have that Connection, Airflow falls back to its own metadata database - which is what allows only some Connections to live in the backend while others stay in Airflow's own DB.

Q4. What's the practical difference between DatabricksRunNowOperator and DatabricksSubmitRunOperator?

Show Answer

DatabricksRunNowOperator triggers a run of a job already defined in the Databricks UI. DatabricksSubmitRunOperator submits a one-off run with the cluster and notebook/script specified inline in the Airflow DAG itself, with no persistent job definition required.

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