- A
Dataproc Serverless (Spark)
Dataproc Serverless runs Spark jobs without cluster management, supports autoscaling, and integrates with Cloud Storage and BigQuery.
- B
Dataproc on GKE
Dataproc on GKE allows running Spark on Kubernetes with autoscaling and managed infrastructure, meeting the requirements.
- C
Standard Dataproc cluster with preemptible workers
Why wrong: Standard Dataproc requires manual cluster management and does not offer serverless pay-per-use. Preemptible workers can reduce cost but still require cluster management.
- D
Cloud Composer with Spark
Why wrong: Cloud Composer is a workflow orchestrator, not a data processing service. It can trigger Spark jobs but does not provide the processing environment itself.
- E
Dataflow with Spark Runner
Why wrong: Dataflow is designed for Beam pipelines, not Spark jobs directly. The Spark Runner is an option but adds complexity and is not the primary use case.
PDE Designing Data Processing Systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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 company is migrating their on-premises Hadoop/Spark workloads to Google Cloud. They need a fully managed service that supports existing Spark jobs with minimal code changes, allows autoscaling, and provides integration with Cloud Storage and BigQuery. The team also wants to avoid managing cluster infrastructure and pay only for what they use. Which TWO services meet these requirements? (Choose two.)
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 Serverless (Spark)
Dataproc Serverless allows running Spark jobs without managing clusters, with autoscaling and pay-per-use pricing. Dataproc on GKE enables running Spark on Kubernetes with autoscaling and is fully managed. Standard Dataproc requires cluster management and is not serverless. Dataflow is for Beam, not Spark. Cloud Composer is for orchestration, not data processing.
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.
- ✓
Dataproc Serverless (Spark)
Why this is correct
Dataproc Serverless runs Spark jobs without cluster management, supports autoscaling, and integrates with Cloud Storage and BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Dataproc on GKE
Why this is correct
Dataproc on GKE allows running Spark on Kubernetes with autoscaling and managed infrastructure, meeting the requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Standard Dataproc cluster with preemptible workers
Why it's wrong here
Standard Dataproc requires manual cluster management and does not offer serverless pay-per-use. Preemptible workers can reduce cost but still require cluster management.
- ✗
Cloud Composer with Spark
Why it's wrong here
Cloud Composer is a workflow orchestrator, not a data processing service. It can trigger Spark jobs but does not provide the processing environment itself.
- ✗
Dataflow with Spark Runner
Why it's wrong here
Dataflow is designed for Beam pipelines, not Spark jobs directly. The Spark Runner is an option but adds complexity and is not the primary use case.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
Designing Data Processing Systems — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Designing Data Processing Systems — This question tests Designing Data Processing Systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Dataproc Serverless (Spark) — Dataproc Serverless allows running Spark jobs without managing clusters, with autoscaling and pay-per-use pricing. Dataproc on GKE enables running Spark on Kubernetes with autoscaling and is fully managed. Standard Dataproc requires cluster management and is not serverless. Dataflow is for Beam, not Spark. Cloud Composer is for orchestration, not data processing.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This PDE 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 PDE exam.
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