Cloud Digital Leader Why Cloud Technology Can Transform Business Practice Question
A data analytics team needs to run complex SQL queries on a large dataset stored in Cloud Storage (CSV files). They want a serverless solution that does not require managing infrastructure. 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 data warehouse that can query data in Cloud Storage via external tables or direct loading. Cloud Dataproc is managed Hadoop/Spark, not serverless for SQL. Cloud Dataflow is for stream/batch processing. Cloud SQL is a managed relational database.
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 Dataflow
Why it's wrong here
Dataflow is a batch and stream processing engine that requires writing pipeline code in Java or Python, not direct SQL execution on CSV files in Cloud Storage. The team needs to run complex SQL queries without managing infrastructure, which BigQuery’s serverless SQL engine provides natively. Dataflow is tempting because it can process large datasets from Cloud Storage, but it demands pipeline development and infrastructure configuration, failing the stem’s requirement for a purely SQL-driven, zero-management solution.
- ✗
Cloud Dataproc
Why it's wrong here
Cloud Dataproc is a managed service for Apache Hadoop and Spark clusters, designed for distributed batch or stream processing using programming frameworks like Spark DataFrames or PySpark. Although Spark SQL can run SQL queries, running it typically requires you to provision and manage clusters, configure scaling, and monitor nodes, which contradicts the requirement for a serverless, zero-management SQL solution. For ad-hoc interactive SQL over external files, Dataproc adds unnecessary operational overhead and does not provide native serverless querying.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a fully managed relational database service (MySQL, PostgreSQL, SQL Server) with a fixed schema and transactional storage engine. It is not a serverless SQL analytics engine and cannot directly query CSV files stored in Cloud Storage; data must be imported and loaded into tables first. Its design optimizes for point lookups and OLTP workloads, not complex analytic queries over external data, so using it would require an ETL process and still leave you managing instance sizing and connections.
- ✓
BigQuery
Why this is correct
BigQuery is a serverless, petabyte-scale data warehouse that supports standard SQL directly on data in Cloud Storage through external tables and federated queries. This lets the analytics team run complex SQL against CSV files without loading them first, while the service automatically manages infrastructure scaling, concurrency, and performance. Its built-in optimizations, like columnar storage and dynamic query planning, make it the only option here that fully satisfies the requirement for manage-free, SQL-only complex analysis.
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
Learn chapter
Cloud Digital Transformation
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
Key term
Dataproc
Dataproc is a managed cloud service for running Apache Spark and Apache Hadoop clusters, allowing you to process large datasets quickly and economically.
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JA
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.