- A
Use Cloud Dataproc with Spark Streaming to aggregate data, and use a Spark ML model embedded in the pipeline
Why wrong: Dataproc with Spark Streaming is possible but requires more operational overhead and does not integrate as seamlessly with AI Platform.
- B
Use BigQuery streaming inserts and run scheduled queries that call the ML model
Why wrong: BigQuery streaming inserts and scheduled queries introduce latency in minutes, not real-time.
- C
Use Cloud Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window
Dataflow handles streaming and windowing natively, and AI Platform Prediction provides low-latency model serving.
- D
Use Cloud Functions triggered by Pub/Sub to process each sensor reading individually
Why wrong: Cloud Functions cannot aggregate over time windows; it processes each message independently.
Quick Answer
The correct answer is to use Cloud Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window. This design is correct because Dataflow’s sliding windows natively handle overlapping 5-minute aggregation intervals for streaming Pub/Sub data, while a DoFn can invoke the AI Platform Prediction API for real-time model scoring without requiring custom infrastructure. On the Google Professional Data Engineer exam, this scenario tests your understanding of combining stream processing with ML serving, specifically the ability to distinguish between sliding windows (for continuous, overlapping time intervals) and fixed windows (which reset cleanly). A common trap is choosing fixed windows, which would miss anomalies that span window boundaries. For a memory tip, remember: “Sliding windows slide, fixed windows divide”—when you need overlapping aggregation for anomaly detection, always slide.
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 manufacturing company wants to detect anomalies in sensor data from thousands of IoT devices in real time. The data is streaming into Pub/Sub. The best solution should use a machine learning model served from AI Platform that scores sensor readings aggregated over 5-minute windows. Which pipeline design meets these requirements?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Use Cloud Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window
Option C is correct because Cloud Dataflow's sliding windows natively handle the 5-minute aggregation requirement for streaming data, and its ability to call external services via a DoFn allows integration with AI Platform Prediction for real-time model scoring. This design aligns with the need for low-latency, scalable processing of Pub/Sub streams without managing infrastructure.
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 Dataproc with Spark Streaming to aggregate data, and use a Spark ML model embedded in the pipeline
Why it's wrong here
Dataproc with Spark Streaming is possible but requires more operational overhead and does not integrate as seamlessly with AI Platform.
- ✗
Use BigQuery streaming inserts and run scheduled queries that call the ML model
Why it's wrong here
BigQuery streaming inserts and scheduled queries introduce latency in minutes, not real-time.
- ✓
Use Cloud Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window
Why this is correct
Dataflow handles streaming and windowing natively, and AI Platform Prediction provides low-latency model serving.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Functions triggered by Pub/Sub to process each sensor reading individually
Why it's wrong here
Cloud Functions cannot aggregate over time windows; it processes each message independently.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between stream processing (Dataflow) and batch-oriented services (BigQuery scheduled queries), and the trap here is assuming that BigQuery's streaming inserts combined with scheduled queries can achieve real-time aggregation, when in fact scheduled queries introduce minutes of delay and are not window-aware for sliding time intervals.
Detailed technical explanation
How to think about this question
Cloud Dataflow's sliding windows allow overlapping windows (e.g., every 1 minute with a 5-minute size) to ensure continuous aggregation, and the model inference can be performed in a ParDo that sends HTTP requests to AI Platform Prediction's REST API. This pattern is common in IoT anomaly detection where sensor data must be scored on aggregated metrics like mean or standard deviation to detect drift, and the use of managed services avoids the complexity of Spark cluster tuning.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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 Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window — Option C is correct because Cloud Dataflow's sliding windows natively handle the 5-minute aggregation requirement for streaming data, and its ability to call external services via a DoFn allows integration with AI Platform Prediction for real-time model scoring. This design aligns with the need for low-latency, scalable processing of Pub/Sub streams without managing infrastructure.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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