PDE Designing Data Processing Systems Practice Question
You need to design a data processing system that ingests streaming data from thousands of IoT devices. The data must be processed in real-time to calculate average temperature per device over 1-minute intervals, and the results should be stored in BigQuery for analysis. You want a serverless solution with minimal management. Which combination of Google Cloud services should you use?
⚠ Common exam trap
The trap here is selecting services that are not serverless (like Dataproc) or not suited for stream processing (like Cloud Functions), or using deprecated services like Cloud IoT Core.
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 Pub/Sub for ingestion, Cloud Dataflow for processing, and BigQuery for storage.
The scenario requires serverless ingestion, real-time processing with windowing, and analytical storage. Pub/Sub handles ingestion at scale, Dataflow provides serverless stream processing with windowing to compute 1-minute averages, and BigQuery stores results for analysis. This combination is fully managed and requires minimal operational effort.
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 Pub/Sub for ingestion, Cloud Functions for processing, and BigQuery for storage.
Why it's wrong here
Pub/Sub and BigQuery are appropriate, but Cloud Functions is not designed for stateful stream processing with windowing. It is event-driven and stateless, making it difficult to compute 1-minute averages per device across multiple events. Dataflow is better suited for this type of stream processing with windowing and state management.
- ✓
Cloud Pub/Sub for ingestion, Cloud Dataflow for processing, and BigQuery for storage.
Why this is correct
Pub/Sub is a scalable, serverless messaging service for ingesting streaming data. Dataflow is a serverless, fully managed service for stream processing that can compute 1-minute averages using windowing. BigQuery is a serverless data warehouse for storing and analyzing results. This combination requires minimal management and is ideal for real-time IoT analytics.
- ✗
Cloud Pub/Sub for ingestion, Cloud Dataproc for processing, and Cloud Bigtable for storage.
Why it's wrong here
Pub/Sub is suitable for ingestion, but Dataproc requires cluster management, which is not serverless. Cloud Bigtable is a NoSQL database for high-throughput workloads, not an analytics warehouse; it lacks the SQL and analytical capabilities of BigQuery. This combination does not meet the serverless and analytical storage requirements.
- ✗
Cloud IoT Core for ingestion, Cloud Dataproc for processing, and BigQuery for storage.
Why it's wrong here
Cloud IoT Core has been deprecated and is no longer available for new projects. Dataproc is not serverless; it requires managing clusters, which adds operational overhead. While BigQuery is suitable for storage, the ingestion and processing components do not meet the requirement for a serverless, minimal-management solution.
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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JA
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 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.