PDE Ingesting and Processing the Data Practice Question
A data engineer needs to ingest data from a Cloud Storage bucket into BigQuery. The data is in CSV format and is updated daily with new files. The engineer wants to minimize manual intervention and ensure that new files are automatically loaded into BigQuery. Which Google Cloud service should be used to orchestrate this?
⚠ Common exam trap
The trap here is overlooking event-driven options and defaulting to scheduled or orchestrated solutions, which add unnecessary complexity for simple file arrival triggers.
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
✓
Cloud Functions
Cloud Functions provides an event-driven, serverless way to respond to new files in Cloud Storage. By configuring a Cloud Function to trigger on object finalize events, the function can execute a BigQuery load job for each new CSV file. This approach requires no polling, minimal code, and automatically scales. Other services like Cloud Scheduler or Cloud Composer could work but involve more overhead and are less direct for this specific requirement.
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 Functions
Why this is correct
Cloud Functions can be triggered by object finalize events in Cloud Storage, allowing automatic execution of code when a new file is uploaded. This enables a serverless, event-driven approach to load new CSV files into BigQuery with minimal intervention. It is simple, cost-effective, and directly addresses the need for automatic ingestion upon file arrival.
- ✗
Cloud Scheduler
Why it's wrong here
Cloud Scheduler is a cron job service that can trigger jobs at specified intervals. While it could be used to periodically run a load job, it does not inherently detect new files in Cloud Storage. It requires additional components to check for new files and trigger loads, making it less automated and more complex than necessary for this scenario.
- ✗
Cloud Data Fusion
Why it's wrong here
Cloud Data Fusion is a fully managed data integration service that provides a graphical interface for building pipelines. It can ingest data from Cloud Storage to BigQuery, but it is designed for more complex ETL scenarios. For simple daily file loads, it introduces unnecessary complexity and cost compared to lighter-weight options that natively handle file arrival events.
- ✗
Cloud Composer
Why it's wrong here
Cloud Composer is a managed Apache Airflow service that can orchestrate complex workflows, including detecting new files and loading them into BigQuery. However, it is a heavyweight solution for a simple daily file load. It involves significant setup and cost, and while it can solve the problem, it is not the most streamlined or cost-effective choice for straightforward ingestion.
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.