The answer is Cloud Dataproc, the managed Spark service on GCP that data analytics teams should use for fast, dynamic, and integrated job execution. Cloud Dataproc is correct because it provisions clusters in under 90 seconds, supports autoscaling to handle variable workloads, and natively integrates with Cloud Storage through the gs:// connector and BigQuery via the BigQuery Storage API and Spark BigQuery connector, eliminating the need for manual data movement. On the Google Associate Cloud Engineer exam, this question tests your understanding of which GCP service provides a fully managed Spark and Hadoop environment versus serverless options like Dataproc Serverless or Dataflow; a common trap is confusing Dataproc with Dataprep or assuming BigQuery itself runs Spark. Remember the memory tip: “Dataproc delivers dynamic, pre-configured clusters for Spark—fast to start, easy to integrate.”
Google ACE Planning and configuring a cloud solution Practice Question
This ACE practice question tests your understanding of planning and configuring a cloud solution. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Why wrong: Cloud Dataflow runs Apache Beam pipelines — it doesn't natively run Apache Spark jobs.
B
Cloud Dataproc
Cloud Dataproc is the managed Apache Spark/Hadoop service on GCP. It integrates directly with Cloud Storage and BigQuery, and supports ephemeral cluster models for cost efficiency.
C
Cloud Composer
Why wrong: Cloud Composer is managed Apache Airflow for workflow orchestration — it can trigger Dataproc jobs but doesn't execute Spark itself.
D
Cloud Run with a custom Spark container
Why wrong: Cloud Run is designed for HTTP containerized services — running Spark on Cloud Run adds significant complexity without the managed cluster benefits of Dataproc.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 runs Apache Beam pipelines — it doesn't natively run Apache Spark jobs.
✓
Cloud Dataproc
Why this is correct
Cloud Dataproc is the managed Apache Spark/Hadoop service on GCP. It integrates directly with Cloud Storage and BigQuery, and supports ephemeral cluster models for cost efficiency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Cloud Composer
Why it's wrong here
Cloud Composer is managed Apache Airflow for workflow orchestration — it can trigger Dataproc jobs but doesn't execute Spark itself.
✗
Cloud Run with a custom Spark container
Why it's wrong here
Cloud Run is designed for HTTP containerized services — running Spark on Cloud Run adds significant complexity without the managed cluster benefits of Dataproc.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Cloud Dataproc uses the Hadoop YARN resource manager for cluster management and can automatically scale worker nodes based on YARN memory metrics via the Dataproc autoscaling policy. Under the hood, it leverages the BigQuery Storage API to read data directly from BigQuery tables without intermediate storage, and the gs:// connector allows Spark to access Cloud Storage as if it were HDFS, enabling decoupled compute and storage. A real-world scenario is running ETL pipelines that read from Cloud Storage, join with BigQuery tables, and write results back, all within a single Dataproc cluster that scales down to zero when idle.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this ACE question in full detail.
Planning and configuring a cloud solution — This question tests Planning and configuring a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this ACE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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