PDE Designing Data Processing Systems Practice Question
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.
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.
- ✓
Dataproc on GKE
Why this is correct
Dataproc on GKE allows running Spark on Kubernetes with autoscaling and managed infrastructure, meeting the requirements.
- ✗
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.
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 |
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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.