Cloud Digital Leader Google Cloud Products and Services Practice Question
A company wants to analyse streaming data from IoT devices in real time with sub-second latency, using SQL queries. Which combination of services should they use?
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 + Dataflow + BigQuery
Dataflow with unbounded sources (like Pub/Sub) and SQL via Beam SQL or Dataflow SQL can process streaming data with low latency. BigQuery can also stream data but with higher latency (seconds). Cloud Functions is not ideal for real-time SQL analytics. Dataproc is for batch 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.
- ✗
Cloud IoT Core + Cloud Functions + Bigtable
Why it's wrong here
Cloud IoT Core is deprecated and intended for device management and telemetry ingestion, not for analytics, while Cloud Functions is synchronous, event-driven compute that invokes per message—it lacks native support for continuous SQL queries, windowing, or aggregation. Bigtable is a NoSQL wide-column database optimized for low-latency point reads and time-series lookups, but it cannot run standard SQL analytics or complex joins, so this stack fails to deliver real-time SQL insights.
- ✗
Cloud Pub/Sub + Dataproc + Cloud Storage
Why it's wrong here
Dataproc is a managed Spark/Hadoop service designed primarily for batch processing; even when used in streaming mode via Spark Streaming, it requires a persistently running cluster and adds startup/teardown overhead, making sub-second SQL analytics unrealistic. Cloud Storage is an object store for data lakes and long-term retention, not a low-latency query engine. Without an interactive SQL layer, this batch-oriented architecture introduces significant latency and operational complexity, so it cannot meet the real-time stream analysis requirement.
- ✗
Cloud Pub/Sub + Cloud Functions + Cloud SQL
Why it's wrong here
Cloud Functions is event-driven serverless compute that executes on individual messages, so it cannot maintain a persistent streaming pipeline or perform stateful aggregation, windowing, or SQL queries across the stream. Cloud SQL is a transactional relational database (OLTP) with connection limits and is not designed for high-throughput, high-velocity analytical workloads; writing each event individually from a function to Cloud SQL creates bottlenecks and lacks the columnar storage and query acceleration required for real-time SQL analytics.
- ✓
Cloud Pub/Sub + Dataflow + BigQuery
Why this is correct
Pub/Sub ingests the IoT data stream durably and asynchronously, Dataflow (Apache Beam) processes it in a fully managed, autoscaling manner with sub-second latency, exactly-once semantics, and support for event-time windows, filters, and enrichments, then writes results to BigQuery using the Storage Write API. BigQuery is a fully managed, serverless, columnar data warehouse that provides native SQL analytics over the streamed data, making this combination the canonical GCP architecture for real-time streaming SQL.
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
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Cloud Digital Transformation
Key term
Dataproc
Dataproc is a managed cloud service for running Apache Spark and Apache Hadoop clusters, allowing you to process large datasets quickly and economically.
Key term
Dataflow
Dataflow is a Google Cloud managed service that processes and transforms data in real-time or batch mode using Apache Beam pipelines.
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JA
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.