Cloud Digital Leader Google Cloud Products and Services Practice Question
A company needs to perform interactive SQL analytics on petabytes of data without managing any infrastructure. They need to query data stored in Cloud Storage and want the fastest query performance. Which Google Cloud service should they use?
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, highly scalable data warehouse that supports SQL queries on data stored in Cloud Storage (external tables) or natively. It provides fast performance on petabyte-scale data without infrastructure management. Dataflow is for ETL, not ad-hoc analytics; Cloud SQL is for OLTP; Looker is a BI layer on top of a data warehouse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
BigQuery
Why this is correct
BigQuery is a fully managed, serverless data warehouse that separates storage from compute, enabling interactive SQL queries over petabytes of data via a high-speed columnar execution engine. Its architecture, using the Dremel query engine, distributes queries across thousands of nodes, delivering sub-second to seconds response times on massive datasets without requiring infrastructure provisioning. This makes it the ideal choice for running ad-hoc, interactive analytics on petabyte-scale data.
- ✗
Looker
Why it's wrong here
Looker is a business intelligence and data visualization platform, not a data warehouse or query engine. It compiles LookML into SQL queries and sends those queries to an underlying database such as BigQuery, Snowflake, or Redshift, then renders results as dashboards and reports. Because Looker relies on an external system for query execution, it cannot perform analytics on petabyte-scale data independently and is therefore not the correct answer for interactive SQL analytics.
- ✗
Dataflow
Why it's wrong here
Dataflow is a unified stream and batch data processing service built on Apache Beam, designed for building and running pipelines that transform, enrich, or move data in real time or at scale. It does not provide an interactive SQL query surface for analysts to run ad-hoc queries against stored data; instead, it executes code-defined pipelines and writes results to sinks like BigQuery or Cloud Storage. Thus, it is unsuitable for interactive SQL analytics on petabyte-scale data.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a managed relational database service for MySQL, PostgreSQL, and SQL Server, optimized for online transaction processing (OLTP) workloads such as web applications and CRM systems. It is designed for low-latency point reads/writes and transactions, not for analytical queries over petabytes of data, which would strain its vertical scaling limits and storage capacity. Consequently, Cloud SQL cannot deliver the performance or scale required for interactive SQL analytics on huge datasets.
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 |
Go deeper
Related to this question
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Cloud Digital Transformation
Key term
ETL
ETL stands for Extract, Transform, Load, a process that moves data from multiple source systems into a single database, data warehouse, or data lake for analysis and reporting.
Key term
Serverless
Serverless is a cloud computing model where the cloud provider manages the servers, and you only pay for the actual compute time your code uses, without having to worry about provisioning or maintaining infrastructure.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.