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 startup is building a real-time dashboard that shows aggregated metrics from social media feeds. They expect up to 10,000 events per second. The data must be near-real-time (< 30 seconds latency) and stored in BigQuery for historical analysis. They have limited experience managing infrastructure. The CTO suggests using Apache Kafka on Compute Engine for ingestion. However, the data engineer recommends a fully managed solution. Which approach should the team adopt?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Cloud Pub/Sub for ingestion and Cloud Dataflow for streaming into BigQuery
Option C is correct because Cloud Pub/Sub provides a fully managed, scalable ingestion service that can handle 10,000+ events per second without infrastructure management, and Cloud Dataflow offers exactly-once, auto-scaling streaming into BigQuery with sub-30-second latency. This combination meets the near-real-time requirement while eliminating operational overhead, aligning with the data engineer's recommendation for a fully managed solution.
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.
✗
Use Cloud Functions to ingest events directly into BigQuery
Why it's wrong here
Cloud Functions is synchronous and may timeout or throttle at high throughput.
✗
Use Apache Kafka on Compute Engine for ingestion, then use Dataflow to write to BigQuery
Why it's wrong here
Managing Kafka on VMs adds operational overhead and is not fully managed.
✓
Use Cloud Pub/Sub for ingestion and Cloud Dataflow for streaming into BigQuery
Read the scenario before looking for a memorised answer.
✗
Use App Engine to receive events and write to BigQuery
Why it's wrong here
App Engine is not optimized for high-throughput stream ingestion.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may choose Option B (Kafka on Compute Engine) because Kafka is a common streaming tool, but the question emphasizes limited infrastructure experience and a fully managed solution, making the self-managed Kafka approach a distraction that ignores operational overhead.
Detailed technical explanation
How to think about this question
Cloud Pub/Sub uses a pull-based subscription model with configurable acknowledgment deadlines (default 10 seconds, max 600 seconds) to ensure at-least-once delivery, while Cloud Dataflow's streaming engine leverages autoscaling workers and checkpointing to handle backpressure and exactly-once semantics into BigQuery's streaming inserts (which have a 1-second latency SLA). In real-world scenarios, this architecture can scale to millions of events per second by partitioning topics and using flow control, whereas Kafka on Compute Engine would require manual tuning of replication factors and broker counts to avoid data loss during node failures.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Use Cloud Pub/Sub for ingestion and Cloud Dataflow for streaming into BigQuery — Option C is correct because Cloud Pub/Sub provides a fully managed, scalable ingestion service that can handle 10,000+ events per second without infrastructure management, and Cloud Dataflow offers exactly-once, auto-scaling streaming into BigQuery with sub-30-second latency. This combination meets the near-real-time requirement while eliminating operational overhead, aligning with the data engineer's recommendation for a fully managed solution.
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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