PDE Storing the Data Practice Question
A company wants to run complex analytical queries on structured data without managing infrastructure. The data volume is terabytes and queries can take seconds to minutes. Which service is appropriate?
⚠ Common exam trap
Google Cloud exams often test the distinction between OLTP databases (Cloud SQL, Firestore, Bigtable) and OLAP/data warehouse services (BigQuery), where candidates mistakenly choose Cloud SQL for analytical workloads due to its SQL familiarity, ignoring its scalability and performance limitations for large-scale 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 correct because it is a serverless, highly scalable data warehouse designed for running complex analytical queries on terabytes of data with fast query performance (seconds to minutes) without any infrastructure management. It uses a columnar storage format and a distributed query engine to handle large-scale structured data efficiently, making it ideal for this use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Firestore
Why it's wrong here
Firestore is a document database for mobile and web application state, not analytical querying; it cannot run complex aggregations across terabytes. It would be correct for storing user profiles or session data needing real-time synchronisation to client apps.
- ✗
Cloud Bigtable
Why it's wrong here
Cloud Bigtable is a wide-column NoSQL store optimised for high-throughput single-row reads and writes, not complex SQL analytics over terabytes. It would be correct for time-series or IoT workloads needing low-latency key lookups at massive scale.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a managed relational database for transactional workloads, not columnar analytical processing; terabyte scans taking seconds to minutes exceed its design. It would be right for an application needing a managed MySQL or PostgreSQL instance with modest query volumes.
- ✓
BigQuery
Why this is correct
BigQuery suits this scenario because its serverless, columnar architecture executes complex analytical SQL across terabyte datasets without infrastructure provisioning, satisfying the no-management constraint. Its distributed query engine returns results in seconds to minutes, matching the stated latency tolerance, unlike transactional row-store databases or single-node engines.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.