Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A developer is building a mobile backend that receives thousands of events per second from IoT devices. The events must be processed in real time and then stored for analysis. Which set of services should they use?
⚠ Common exam trap
Many exam-takers confuse Cloud Storage as a real-time processing service (it is not—it is a durable object store for batch data) and overlook the need for a stream processing engine like Dataflow to handle real-time transformations before analysis.
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 -> Cloud Dataflow -> BigQuery
Cloud Pub/Sub provides a scalable, fully managed message ingestion service for high-throughput event streams, Cloud Dataflow (based on Apache Beam) enables real-time stream processing with exactly-once semantics and low latency, and BigQuery offers a serverless data warehouse for fast analytical queries on the processed data. This combination handles the requirements of real-time processing and subsequent storage for analysis without operational overhead.
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 -> Cloud Dataflow -> BigQuery
Why this is correct
This pipeline is purpose-built for real-time analytics: Cloud Pub/Sub ingests millions of messages/sec as a scalable, decoupled event bus, Cloud Dataflow (Apache Beam) applies stream processing with exactly-once, sub-second latency, and BigQuery provides columnar storage with powerful SQL analytics. The serverless nature of each service means no infrastructure management, and the integration is native.
- ✗
Cloud IoT Core -> Cloud Storage -> Dataproc
Why it's wrong here
Cloud IoT Core, though for device telemetry, lacks a general-purpose messaging subscription model and is being deprecated; Cloud Storage simply persists files, while Dataproc is optimized for Hadoop/Spark batch jobs over bounded data. This chain cannot deliver sub-second processing because each hop adds delay and the storage-first design forces batch-oriented analysis.
- ✗
Cloud Pub/Sub -> Cloud Storage -> BigQuery
Why it's wrong here
Using Cloud Storage as an intermediary creates a file-arrival event pattern: a Pub/Sub subscription must dump messages into objects, then BigQuery loads them via an external table or load job, introducing minutes of latency and no opportunity for enrichment. It is a batch transfer, not a streaming transformation, so dashboards would lag and the pipeline fails for real-time alerting.
- ✗
Cloud Pub/Sub -> Cloud Functions -> Cloud SQL
Why it's wrong here
Cloud Functions is intended for lightweight, event-driven tasks and caps concurrency per instance, so a high-volume message stream quickly triggers scaling limits and cold starts; Cloud SQL is a relational OLTP database with constraints on rows and transactions, making it an unsuitable analytic store. This architecture can handle low-rate funnels but breaks under load and ad-hoc analytics.
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 |
Go deeper
Related to this question
Learn chapter
Cloud Digital Transformation
Key term
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.