The answer is Pub/Sub → Dataflow → BigQuery. This combination is correct because Pub/Sub acts as a durable, scalable ingestion layer that decouples thousands of IoT devices from downstream processing, while Dataflow’s auto-scaling and exactly-once semantics handle the 10 GB per hour streaming load without manual intervention, writing directly into BigQuery for near-real-time analytics. On the Google Professional Data Engineer exam, this scenario tests your understanding of building a cost-effective IoT data ingestion pipeline that avoids unnecessary intermediate storage or complex orchestration. A common trap is choosing Cloud Storage as a staging layer, which adds latency and cost; the correct path leverages Pub/Sub’s pull-based subscription to buffer bursts and Dataflow’s streaming engine to minimize overhead. Remember the mnemonic “P-D-B” for Pipeline, Decouple, BigQuery—or simply think of it as “ingest, process, analyze” without the middleman.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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 company wants to ingest IoT sensor data from thousands of devices into BigQuery for near-real-time analytics. The data volume is approximately 10 GB per hour. Which combination of Google Cloud services should they use for a cost-effective and scalable solution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Pub/Sub → Dataflow → BigQuery
Pub/Sub provides a scalable, managed ingestion layer for high-volume IoT data, decoupling producers from consumers. Dataflow (Apache Beam) processes the streaming data in near-real-time with exactly-once semantics and auto-scaling, writing directly to BigQuery for analytics. This combination minimizes operational overhead and cost by avoiding intermediate storage and manual scaling.
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.
✓
Pub/Sub → Dataflow → BigQuery
Why this is correct
Pub/Sub ingests events, Dataflow streams them to BigQuery, scaling automatically.
Related concept
Read the scenario before looking for a memorised answer.
✗
Cloud IoT Core → Cloud Functions → BigQuery
Why it's wrong here
Cloud Functions has limits on concurrent executions and may not sustain high throughput.
✗
Cloud IoT Core → Cloud Dataproc → BigQuery
Why it's wrong here
Dataproc adds overhead and is not ideal for streaming ingestion.
✗
Cloud IoT Core → Cloud Storage → BigQuery load jobs
Why it's wrong here
Storage + load is batch, not near-real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Cloud Functions can handle streaming workloads, but its synchronous nature and timeout limit make it unsuitable for sustained high-throughput ingestion, whereas Pub/Sub + Dataflow is the standard pattern for near-real-time analytics.
Detailed technical explanation
How to think about this question
Dataflow uses the Apache Beam SDK to enable windowed aggregations and watermark handling for late-arriving data, critical for IoT sensor timestamps. Under the hood, Pub/Sub uses a pull-based subscription model with exactly-once delivery when combined with Dataflow's checkpointing, avoiding duplicates in BigQuery. In practice, this architecture can handle spikes of 10 GB/hour by auto-scaling Dataflow workers based on Pub/Sub backlog, while Cloud Functions would require a fan-out pattern with multiple invocations per second, hitting concurrency limits.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Pub/Sub → Dataflow → BigQuery — Pub/Sub provides a scalable, managed ingestion layer for high-volume IoT data, decoupling producers from consumers. Dataflow (Apache Beam) processes the streaming data in near-real-time with exactly-once semantics and auto-scaling, writing directly to BigQuery for analytics. This combination minimizes operational overhead and cost by avoiding intermediate storage and manual scaling.
What should I do if I get this PDE 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.
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Question Discussion
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