- A
Ingest via Cloud IoT Core directly to Cloud Bigtable, then query with BigQuery.
Why wrong: Cloud IoT Core is for device management, not high-volume event ingestion; direct write to Bigtable may lack stream processing.
- B
Ingest via Cloud Pub/Sub, process with Cloud Dataproc, store in Cloud Storage, and query with BigQuery.
Why wrong: Cloud Dataproc (Hadoop/Spark) has higher latency for streaming; Cloud Storage is not optimized for sub-second queries.
- C
Ingest via Cloud Pub/Sub, store raw data in Cloud Storage, and use Cloud SQL for aggregations.
Why wrong: Cloud SQL is not designed for high write throughput or sub-second aggregations on large datasets.
- D
Ingest via Cloud Pub/Sub, process with Cloud Dataflow, store in Cloud Bigtable, and query from the dashboard.
This combination handles high ingest rates, stream processing, and low-latency queries.
Quick Answer
The correct architecture for a real-time IoT analytics dashboard with unpredictable, high-volume data spikes is to ingest via Cloud Pub/Sub, process with Cloud Dataflow, store in Cloud Bigtable, and query from the dashboard. This combination works because Cloud Pub/Sub decouples ingestion from processing, absorbing millions of events per second without data loss, while Cloud Dataflow’s exactly-once stream processing computes sub-second aggregations, and Cloud Bigtable’s low-latency, high-throughput NoSQL storage serves those results directly to the dashboard. On the Google Professional Cloud Architect exam, this scenario tests your ability to distinguish between storage options for real-time serving—Bigtable for high-write, low-latency queries versus BigQuery for analytical batch queries—and a common trap is choosing BigQuery for the dashboard, which adds seconds of latency. Remember the mnemonic “P-D-B” for Pipeline, Dataflow, Bigtable: the three pillars of streaming IoT architecture.
Google PCA Design and plan a cloud solution architecture Practice Question
This PCA practice question tests your understanding of design and plan a cloud solution architecture. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Cloud IoT Core is for device management, not high-volume event ingestion; direct write to Bigtable may lack stream processing.
- ✗
Ingest via Cloud Pub/Sub, process with Cloud Dataproc, store in Cloud Storage, and query with BigQuery.
Why it's wrong here
Cloud Dataproc (Hadoop/Spark) has higher latency for streaming; Cloud Storage is not optimized for sub-second queries.
- ✗
Ingest via Cloud Pub/Sub, store raw data in Cloud Storage, and use Cloud SQL for aggregations.
Why it's wrong here
Cloud SQL is not designed for high write throughput or sub-second aggregations on large datasets.
- ✓
Ingest via Cloud Pub/Sub, process with Cloud Dataflow, store in Cloud Bigtable, and query from the dashboard.
Why this is correct
This combination handles high ingest rates, stream processing, and low-latency queries.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Cloud Dataflow uses the Apache Beam SDK to perform windowed aggregations (e.g., sliding windows) with exactly-once processing semantics, enabling sub-second latency via its streaming engine. Cloud Bigtable's wide-column store supports high write throughput (up to millions of rows per second) and single-row reads in under 10ms, making it ideal for serving real-time dashboard queries without the overhead of SQL joins or indexing.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCA question test?
Design and plan a cloud solution architecture — This question tests Design and plan a cloud solution architecture — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Ingest via Cloud Pub/Sub, process with Cloud Dataflow, store in Cloud Bigtable, and query from the dashboard. — Option D is correct because 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.
What should I do if I get this PCA question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
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.
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