Cloud Digital Leader Fundamental Cloud Concepts Practice Question
A data analytics team processes large datasets using Apache Spark. They want a fully managed service that allows them to run Spark jobs without provisioning clusters. 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
✓
Dataproc
Dataproc is a managed Spark and Hadoop service. With Dataproc, you can create clusters quickly, but the fully managed serverless option is Dataproc Serverless, which runs Spark jobs without cluster management.
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 it's wrong here
BigQuery is a fully managed, serverless data warehouse optimized for SQL-based analytics on large datasets. It does not provide Apache Spark execution engines or cluster infrastructure, so Spark applications cannot run natively within BigQuery. Therefore, for teams requiring the flexibility of Spark's non-SQL workloads, BigQuery is not the appropriate service.
- ✗
Dataflow
Why it's wrong here
Dataflow is Google Cloud's unified stream and batch processing platform built around Apache Beam, where pipelines are defined using Beam SDKs (Java, Python, Go). While Beam has a Spark runner, Dataflow itself does not host or execute native Apache Spark jobs; it manages Beam workers that execute Beam transformations. Thus, using Dataflow to run existing Spark code would require rewriting the application as a Beam pipeline, making it the wrong choice for direct Spark support.
- ✓
Dataproc
Why this is correct
Dataproc is Google Cloud's managed service for running Apache Spark and Hadoop clusters, providing native Spark support with custom cluster configurations, autoscaling, and integration with cloud storage. Dataproc Serverless further allows teams to run Spark jobs without provisioning or managing any cluster infrastructure at all. This makes Dataproc the straightforward choice for a team already using Spark.
- ✗
Cloud Dataprep
Why it's wrong here
Cloud Dataprep is a serverless, visual data preparation tool built on Cloud Dataflow, designed for exploring and cleansing data interactively without writing code. It does not execute Spark jobs or provide a Spark runtime; instead, it generates and runs Dataflow pipelines behind the scenes. Therefore, it cannot serve as a platform for running existing Spark applications.
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
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Cloud Digital Transformation
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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
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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.