PDE Designing Data Processing Systems Practice Question
A media company needs to process a large number of small JSON files stored in Cloud Storage. They want to use a serverless, SQL-based approach to transform and aggregate the data without managing infrastructure. Which Google Cloud service should they use?
⚠ Common exam trap
Candidates often confuse serverless with code-free SQL; Dataflow is serverless but requires coding, while BigQuery is both serverless and SQL-based.
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
BigQuery is a serverless, SQL-based analytics service that can query data directly from Cloud Storage, including JSON files, using external tables or loading jobs. It eliminates infrastructure management and allows the media company to transform and aggregate data using familiar SQL. This makes it the most suitable choice among the options.
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 Dataproc
Why it's wrong here
Cloud Dataproc is a managed Spark and Hadoop service, but it requires cluster provisioning and management, which contradicts the serverless requirement. It also typically requires writing Spark code, not SQL. Although Hive or Spark SQL can be used, it is not as seamless as BigQuery for ad-hoc SQL on Cloud Storage data.
- ✗
Cloud Dataflow
Why it's wrong here
Cloud Dataflow is a serverless data processing service, but it requires writing Apache Beam code in Java or Python. It is not SQL-based, so it does not meet the requirement for a SQL-based approach without coding. While it can process JSON files, it is not the best fit for a code-free, SQL-centric solution.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a managed relational database service for MySQL, PostgreSQL, and SQL Server. It is not designed for large-scale data processing or querying files in Cloud Storage. It would require loading data into the database, which is impractical for a large number of JSON files. It is not serverless in the sense of automatic scaling for analytics.
- ✓
BigQuery
Why this is correct
BigQuery is a serverless, highly scalable data warehouse that supports SQL queries. It can directly query external data in Cloud Storage using external tables or load JSON files. This allows the company to transform and aggregate data using SQL without managing infrastructure. It is the ideal service for this requirement.
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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JA
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.