PDE Designing Data Processing Systems Practice Question
A healthcare analytics team needs to run a series of SQL transformations on data stored in BigQuery. The transformations must run on a schedule, and the team wants to minimize operational overhead by using a fully managed service that integrates with BigQuery and Cloud Logging. They also need to parameterize the SQL queries with runtime values such as the current date. Which Google Cloud service should they use?
⚠ Common exam trap
The trap here is overcomplicating a simple scheduled SQL task by choosing a general-purpose orchestrator or compute service instead of the native BigQuery scheduling feature.
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
✓
BigQuery scheduled queries, using the @run_date parameter for runtime values.
BigQuery scheduled queries are a native, fully managed feature that executes SQL on a defined schedule. They support parameterization with system variables like @run_date, which allows dynamic date-based filtering. Integration with Cloud Logging provides visibility. This eliminates the need to manage infrastructure or write code, perfectly matching the requirement for a low-overhead, scheduled SQL transformation solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Composer with a DAG that uses BigQueryInsertJobOperator to run the SQL queries.
Why it's wrong here
Cloud Composer can schedule and parameterize BigQuery jobs, but it requires managing an Airflow environment, which adds operational overhead. The team wants a fully managed, lower-overhead solution. While Composer is powerful for complex workflows, it is not the minimal-overhead choice for simple scheduled SQL transformations.
- ✓
BigQuery scheduled queries, using the @run_date parameter for runtime values.
Why this is correct
BigQuery scheduled queries are a fully managed feature that runs SQL on a schedule, supports parameterization with @run_date and other system variables, and integrates with Cloud Logging for monitoring. It requires no infrastructure management, making it ideal for scheduled SQL transformations. This directly meets the team's requirements with minimal operational overhead.
- ✗
Dataflow with a pipeline that reads from BigQuery, applies SQL transformations using Beam SQL, and writes back to BigQuery.
Why it's wrong here
Dataflow is a powerful data processing service, but using it for simple scheduled SQL transformations adds unnecessary complexity and cost. It requires pipeline development and management, whereas BigQuery scheduled queries provide a native, serverless scheduling mechanism. Dataflow is better suited for complex, large-scale data processing, not routine SQL scheduling.
- ✗
Cloud Scheduler triggering a Cloud Function that calls the BigQuery API to execute the SQL.
Why it's wrong here
This approach requires writing and maintaining a Cloud Function, handling authentication, and managing errors. It introduces more operational overhead than a native BigQuery feature. While it is flexible, it is not the fully managed, low-overhead solution the team seeks for scheduled SQL transformations.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.