PDE Ingesting and Processing the Data Practice Question
A company wants to transform data using dbt (data build tool) on BigQuery. They have a CI/CD pipeline and need to version-control their transformations. Which setup is recommended?
⚠ Common exam trap
This question tests the distinction between orchestration (Cloud Composer) and CI/CD (Cloud Build), so candidates mistakenly choose Cloud Composer because they think scheduling equals version control, but the question explicitly requires version control and CI/CD, not just scheduling.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy dbt models in a Cloud Build pipeline that runs dbt run
Dbt is designed for version-controlled, SQL-based transformations, and integrating it with Cloud Build allows you to run `dbt run` as part of a CI/CD pipeline. This setup ensures that every change to dbt models is automatically tested and deployed, which aligns with the requirement for version control and automated deployment on BigQuery.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create Dataflow pipelines for each transformation
Why it's wrong here
Dataflow is a streaming and batch execution engine; it runs pipelines but provides no SQL model definitions, dependency graph or version-controlled transformation repository, so CI/CD cannot diff or test changes. It is tempting because Dataflow genuinely orchestrates BigQuery jobs, and would suit large-scale custom Apache Beam processing rather than dbt-managed transformations.
- ✓
Deploy dbt models in a Cloud Build pipeline that runs dbt run
Why this is correct
Running dbt models inside a Cloud Build pipeline executes the transformations on BigQuery while the dbt project files remain in source control, giving versioned, repeatable deployments. This satisfies the CI/CD and version-control constraints without manual execution.
- ✗
Use Cloud Composer to orchestrate dbt jobs
Why it's wrong here
Cloud Composer schedules and orchestrates workloads but does not itself store transformation logic, resolve model dependencies or compile SQL, so version control of the transformations still requires dbt project files in Git. It is tempting because Composer genuinely triggers dbt runs on a schedule, and would be correct for orchestrating dbt alongside other pipeline tasks.
- ✗
Run dbt directly on BigQuery using scripting
Why it's wrong here
Ad-hoc scripting on BigQuery bypasses dbt's project structure, ref() dependency resolution and compiled artefacts, leaving nothing meaningful for CI/CD to version, test or promote between environments. It is tempting because scripts execute transformations directly, and would be reasonable for one-off exploratory queries rather than governed, repeatable transformation code.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PDE question from scratch — 747 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.