PDE Ingesting and Processing the Data Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Use Cloud Composer (Airflow) to schedule and run dbt commands.

  Why this is correct

  Cloud Composer runs Airflow DAGs that invoke dbt commands, giving dependency management, retries and hourly scheduling across both full-refresh and incremental models. Airflow's operators orchestrate the dbt runs and surface failures, which raw cron or Cloud Scheduler cannot manage as cleanly.

• ✗

  Use Cloud Build with a trigger to run dbt every hour.

  Why it's wrong here

  Cloud Build triggers fire on repository events such as commits or tags, not on a recurring hourly timetable, so nothing initiates the dbt runs on schedule. It is tempting because Cloud Build genuinely suits CI/CD pipelines that execute dbt on code changes, which is a different requirement from time-based orchestration.

• ✗

  Use Cloud Scheduler to trigger a Cloud Function that runs dbt.

  Why it's wrong here

  Cloud Scheduler plus a Cloud Function executes dbt in a short-lived serverless runtime with a timeout ceiling, unsuitable for long full refreshes and incremental runs. It is tempting because it is fully managed and event-driven, and would suit lightweight, short-lived scheduled tasks.

• ✗

  Set up a cron job on a Compute Engine instance to run dbt.

  Why it's wrong here

  A Compute Engine cron job runs dbt on a persistent VM, requiring manual scheduling, credential handling and no native retry or dependency orchestration. It is tempting because cron is familiar and works for simple scripts, and would suit a one-off batch job outside a managed pipeline.

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