Google PCA Design and plan a cloud solution architecture Practice Question
A startup is developing a real-time analytics dashboard that ingests data from IoT devices. The data volume is unpredictable but can spike to millions of events per second. The dashboard must display near real-time aggregations with sub-second latency. Which Google Cloud architecture should the architect recommend?
⚠ Common exam trap
Candidates often choose batch-oriented services like BigQuery or Dataproc for real-time requirements, overlooking that Cloud Dataflow's stream processing and Cloud Bigtable's low-latency storage are specifically designed for sub-second, high-throughput dashboard use cases.
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
✓
Ingest via Cloud Pub/Sub, process with Cloud Dataflow, store in Cloud Bigtable, and query from the dashboard.
Cloud Pub/Sub provides scalable, asynchronous ingestion for unpredictable IoT data spikes, Cloud Dataflow enables stream processing for near real-time aggregations with sub-second latency, and Cloud Bigtable offers low-latency, high-throughput storage ideal for serving aggregated results directly to a dashboard. This combination meets the requirements of unpredictable volume, real-time processing, and low-latency queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ingest via Cloud IoT Core directly to Cloud Bigtable, then query with BigQuery.
Why it's wrong here
Bigtable serves low-latency key lookups, not sub-second aggregation queries; BigQuery adds seconds of latency and Cloud IoT Core is retired. It is tempting because Bigtable scales to millions of writes per second, which suits high-volume ingestion, but the dashboard needs streaming aggregation, not wide-column storage.
- ✗
Ingest via Cloud Pub/Sub, process with Cloud Dataproc, store in Cloud Storage, and query with BigQuery.
Why it's wrong here
Dataproc is batch-oriented; its micro-batch latency cannot meet sub-second dashboard aggregation. It is tempting because Dataproc handles large-scale processing and BigQuery aggregates well, but the stem demands continuous streaming with sub-second latency, which Cloud Dataflow provides instead.
- ✗
Ingest via Cloud Pub/Sub, store raw data in Cloud Storage, and use Cloud SQL for aggregations.
Why it's wrong here
Cloud SQL cannot sustain sub-second aggregation over millions of events per second, and Cloud Storage introduces retrieval latency. It is tempting because Pub/Sub decouples ingestion and Cloud SQL is familiar, but the requirement is streaming aggregation at scale, which Cloud Dataflow with BigQuery or Bigtable serves.
- ✓
Ingest via Cloud Pub/Sub, process with Cloud Dataflow, store in Cloud Bigtable, and query from the dashboard.
Why this is correct
Cloud Pub/Sub absorbs unpredictable spikes to millions of events per second without backpressure, while Dataflow provides streaming windowed aggregations. Bigtable's row-key design delivers the low-latency point and range reads the dashboard needs, satisfying the sub-second latency constraint that batch warehouses such as BigQuery cannot meet for continuous refreshes.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Pub/Sub
Pub/Sub is a messaging pattern where publishers send messages without knowing who receives them, and subscribers receive only the messages they care about.
Key term
Throughput
Throughput is the rate at which data is successfully transferred from one point to another over a network, typically measured in bits per second.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PCA 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 PCA exam.