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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

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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