PDE Designing Data Processing Systems Practice Question
You need to store petabytes of data in a data warehouse that supports ANSI SQL, automatic scaling, and real-time analytics. The data is primarily used for ad-hoc queries and business intelligence. Which Google Cloud service should you use?
⚠ Common exam trap
It's easy for candidates to confuse a transactional database like Cloud Spanner with an analytical data warehouse, overlooking that BigQuery is purpose-built for large-scale SQL analytics.
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 the correct choice because it is a serverless, petabyte-scale data warehouse that supports ANSI SQL, automatically scales, and is optimized for ad-hoc queries and business intelligence. Its separation of storage and compute allows independent scaling and cost-effective analytics on large datasets.
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 SQL
Why it's wrong here
Cloud SQL is a managed relational database service for MySQL, PostgreSQL, and SQL Server, but it is not designed for petabyte-scale data warehousing. It has limited storage and scaling capabilities compared to BigQuery, and is better suited for transactional workloads. It cannot handle the scale and analytical query patterns described.
- ✗
Cloud Bigtable
Why it's wrong here
Cloud Bigtable is a NoSQL database for large analytical and operational workloads, but it does not support ANSI SQL or ad-hoc queries. It is designed for high-throughput read/write access with low latency, such as for time-series data or IoT, not for business intelligence or SQL-based analytics. It lacks the SQL interface required.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a globally distributed, horizontally scalable relational database that supports ANSI SQL, but it is designed for transactional workloads with strong consistency, not for petabyte-scale analytical queries. It is more expensive for large-scale analytics and does not provide the same columnar storage and query optimization as BigQuery.
- ✓
BigQuery
Why this is correct
BigQuery is a fully managed, petabyte-scale data warehouse that supports ANSI SQL and provides automatic scaling and real-time analytics. It is designed for ad-hoc queries and business intelligence workloads, with separation of storage and compute, and integrates with BI tools. It is the ideal choice for this scenario.
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.