mediumMultiple ChoiceObjective-mapped
Google ACE Practice Question: A data analytics team runs Apache Spark jobs to…
A data analytics team runs Apache Spark jobs to process large datasets. They need a managed cluster that provisions quickly, scales dynamically, and integrates with Cloud Storage and BigQuery. Which service should they use?
⚠ Common exam trap
Candidates often confuse Cloud Dataflow (a Beam-based service) with a managed Spark service, or assume Cloud Run can handle dynamic Spark cluster scaling, when in fact only Cloud Dataproc provides the native Spark runtime and auto-scaling cluster management required for this use case.
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
✓
Cloud Dataproc
Cloud Dataproc is the correct choice because it is a managed Spark and Hadoop service that provisions clusters in under 90 seconds, supports autoscaling, and natively integrates with Cloud Storage (via the gs:// connector) and BigQuery (via the BigQuery Storage API and Spark BigQuery connector). This makes it ideal for teams needing fast, dynamic, and integrated Spark job execution.
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
Cloud Dataflow is a unified programming model for batch and streaming data processing based on Apache Beam, not Apache Spark. It executes Beam pipelines with its own worker orchestration and does not interpret Spark's RDD, DataFrame, or DAG APIs; Spark jobs cannot be submitted directly to Dataflow. While Dataflow can handle many large-scale data problems, it is architecturally tied to Beam, making it the wrong service for running native Spark applications.
- ✓
Cloud Dataproc
Why this is correct
Cloud Dataproc is the fully managed Apache Spark and Hadoop service on Google Cloud, purpose-built to run Spark jobs at scale. It offers fast cluster provisioning via ephemeral clusters, direct connectors to Cloud Storage and BigQuery for reading and writing data without ETL, and automatically manages the HDFS/YARN infrastructure. Because it is native to Spark and integrates with the Google Cloud ecosystem, it is the correct choice for executing Spark workloads.
- ✗
Cloud Composer
Why it's wrong here
Cloud Composer is a managed Apache Airflow service for orchestrating workflows, such as scheduling and coordinating data pipelines across multiple systems, not for executing distributed compute like Spark. It can submit jobs to Dataproc through Airflow operators, but the actual Spark execution happens on Dataproc clusters, not inside Composer itself. Selecting Composer to run Spark would conflate orchestration with compute, which is why this option is incorrect.
- ✗
Cloud Run with a custom Spark container
Why it's wrong here
Cloud Run is a serverless compute platform designed to run stateless HTTP-triggered containers, automatically scaling to zero and handling individual requests within a short timeout. Running an Apache Spark workload requires a long-running, distributed cluster with shuffle, persistent executors, and master-worker coordination, which Cloud Run's request-based model does not provide. A custom Spark container on Cloud Run would need manual cluster orchestration, fail to coordinate distributed state, and incur significant operational complexity without Dataproc's managed benefits.
Go deeper
Related to this question
Learn chapter
Google Cloud Platform Overview
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
Cloud storage
Cloud storage is a service that lets you save data on remote servers accessed over the internet instead of on your computer's hard drive.
About these practice questions
One of 769 original ACE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE 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 ACE exam.