PDE Designing Data Processing Systems Practice Question
A data team is building a near-real-time dashboard that displays aggregated metrics from Kafka topics. They want to use Pub/Sub as a managed messaging service and Dataflow for stream processing. They need to ingest data from Kafka into Pub/Sub with minimal custom code. Which THREE Google Cloud services should they use together? (Choose three.)
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
✓
Dataflow
Option A, Dataflow, is correct because it is Google Cloud's managed Apache Beam service for stream processing, and it can run a streaming pipeline that reads from Pub/Sub, performs windowed aggregations, and writes results for the near-real-time dashboard. Option B, Pub/Sub, is correct because it serves as the managed messaging service that decouples the Kafka ingestion layer from the Dataflow processing layer, buffering messages and enabling reliable, scalable delivery. Option C, Kafka Connect (with Pub/Sub connector), is correct because Kafka Connect provides a configuration-driven, low-code way to move data from Kafka topics into Pub/Sub using a Pub/Sub sink connector, satisfying the requirement for minimal custom code. Option D, Cloud NAT, is not relevant because it provides outbound internet address translation for private VMs and does not ingest Kafka data into Pub/Sub. Option E, Cloud Functions, is not appropriate here because it is an event-driven serverless compute service for lightweight functions, not the managed stream-processing engine needed for aggregated Kafka metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Dataflow
Why this is correct
Dataflow provides the managed Apache Beam runtime that reads from Kafka and writes into Pub/Sub, satisfying the minimal-custom-code constraint. Its built-in Kafka-to-Pub/Sub template performs the ingestion without bespoke connectors, then continues stream processing for the dashboard's aggregated metrics.
- ✓
Pub/Sub
Why this is correct
Pub/Sub acts as the managed messaging layer that decouples Kafka ingestion from Dataflow processing, satisfying the requirement for a managed messaging service. Dataflow reads from it for stream aggregation, enabling near-real-time dashboards without operating brokers.
- ✓
Kafka Connect (with Pub/Sub connector)
Why this is correct
Kafka Connect with the Pub/Sub connector provides a managed, configuration-driven pipeline that streams Kafka topics into Pub/Sub without bespoke producer code, satisfying the minimal custom code constraint. Dataflow then reads from Pub/Sub for aggregation. This avoids writing and maintaining your own Kafka consumer, which the stem explicitly seeks to minimise.
- ✗
Cloud NAT
Why it's wrong here
Cloud NAT provides outbound address translation for private instances; it moves no Kafka records into Pub/Sub. It is tempting because Kafka clusters on private subnets often need NAT for egress, but that is a networking prerequisite, not an ingestion mechanism. The Kafka Connect Pub/Sub connector performs the actual transfer.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions runs event-driven snippets and offers no managed Kafka-to-Pub/Sub connector, so it would require the custom code the stem explicitly avoids. It is tempting for lightweight glue tasks such as transforming individual messages, but the Kafka Connect Pub/Sub connector already provides the required ingestion without bespoke development.
Visual reference
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
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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.