PDE Maintaining and Automating Data Workloads Practice Question
A data engineer has a BigQuery SQL script that must run every day at 06:00, load its results into a reporting table, and retry automatically if the query fails due to transient errors. The team has no existing orchestration tooling, wants the lowest operational overhead, and needs the schedule and the SQL to be managed entirely inside Google Cloud. Which approach should the engineer use?
⚠ Common exam trap
The trap here is reaching for external schedulers or orchestration platforms when BigQuery's own scheduled query feature already covers recurring SQL with retries and destination tables.
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
✓
Create a scheduled query in BigQuery with the desired SQL and a daily schedule, targeting the reporting table as the destination.
BigQuery scheduled queries are the native, serverless way to run SQL on a recurring schedule and write output to a destination table, with automatic retry of failed runs. Because the schedule, SQL, and destination all live inside BigQuery, no additional infrastructure or orchestration tooling is needed, matching the low-overhead requirement precisely.
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 Dataflow with a batch pipeline that reads from the source tables, applies the transformation, and writes to the reporting table on a schedule.
Why it's wrong here
Dataflow is designed for large-scale data processing with Apache Beam, not for scheduling a single SQL statement. Writing a Beam pipeline to express SQL logic reintroduces development and maintenance effort, and the scenario gives no indication that the data volume or transformation complexity requires Dataflow's distributed execution model.
- ✗
Create a Cloud Composer environment and author a single-task DAG that runs the SQL daily with Airflow retries configured.
Why it's wrong here
Cloud Composer would work functionally, but provisioning and maintaining an entire Airflow environment for one daily query is heavyweight and costly. The scenario explicitly states there is no existing orchestration tooling and that low operational overhead is required, which makes a managed Composer environment disproportionate to the task.
- ✓
Create a scheduled query in BigQuery with the desired SQL and a daily schedule, targeting the reporting table as the destination.
Why this is correct
BigQuery scheduled queries let you store SQL, define a schedule, and write results to a destination table directly in the BigQuery UI or API. The service manages execution and retries failed runs automatically, requiring no containers, schedulers, or external orchestration, which is exactly the lowest-overhead option described.
- ✗
Deploy the SQL as a Cloud Run service and create a Cloud Scheduler HTTP job that invokes it daily at 06:00.
Why it's wrong here
Cloud Run can execute BigQuery queries, but the engineer must containerise the SQL, handle authentication, and implement retry behaviour in application code. This adds build and deployment overhead with no existing orchestration tooling, whereas BigQuery already offers a native scheduling mechanism that handles retries for the query itself.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.