PDE Designing Data Processing Systems Practice Question
A company wants to design a data pipeline for real-time fraud detection. The system must process streaming financial transactions, enrich them with user profiles from a lookup table, and flag suspicious activities within seconds. Which architecture pattern would be MOST suitable?
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
✓
Kappa architecture using a single stream processing framework like Apache Beam
Kappa architecture uses a single stream processing engine to handle both real-time and batch reprocessing, simplifying the pipeline. Lambda architecture requires maintaining separate batch and streaming layers, increasing complexity. The scenario only requires real-time processing with enrichment, so Kappa is more appropriate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pub/Sub combined with Cloud Functions for stateless processing
Why it's wrong here
Cloud Functions are stateless and not designed for stream processing with stateful operations like enrichment and windowing.
- ✓
Kappa architecture using a single stream processing framework like Apache Beam
Why this is correct
Kappa processes everything as a stream, suitable for real-time fraud detection with enrichment from a side input.
- ✗
Batch processing with hourly micro-batches using Dataflow
Why it's wrong here
Hourly batches would be too slow for real-time fraud detection.
- ✗
Lambda architecture with a batch layer for historical analysis and a speed layer for real-time processing
Why it's wrong here
Lambda adds unnecessary complexity. The need is purely real-time fraud detection, not batch historical analysis.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.