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?
⚠ Common exam trap
GCDL often tests the difference between 'managed Spark' (Dataproc) and 'managed Beam' (Dataflow) — candidates who see 'large datasets' and 'processing' often jump to Dataflow or BigQuery without noticing the explicit Apache Spark requirement.
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 Google Cloud's fully managed Apache Spark and Hadoop service, letting teams submit Spark jobs to ephemeral or long-running clusters without provisioning infrastructure themselves. It supports Spark, Spark SQL, PySpark, Hive, and Pig, and integrates with Cloud Storage and BigQuery as data sources. That matches the requirement exactly.
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.
Go deeper
Related to this question
Learn chapter
Managed Services vs Unmanaged Services on GCP
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
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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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.