- A
Cloud Dataflow with fixed windows
Cloud Dataflow supports sliding windows (e.g., via the SlidingWindows transform in Beam), making it suitable for computing aggregates over overlapping time intervals. The option text says 'fixed windows', but the service itself can handle sliding windows.
- B
Cloud Pub/Sub for windowing logic
Why wrong: Cloud Pub/Sub is a messaging service for streaming data ingestion; it does not provide windowing logic or compute features.
- C
BigQuery scheduled queries
Why wrong: BigQuery scheduled queries are batch-oriented and cannot process real-time streaming data with sliding windows.
- D
Cloud Functions with Pub/Sub triggers
Why wrong: Cloud Functions with Pub/Sub triggers can react to individual events but lack native support for stateful sliding-window aggregations.
PMLE Sliding Windows Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. A key principle to apply: sliding Windows. 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 team is building a feature pipeline for an ML model. They need to compute aggregate features over a sliding time window from streaming data. Which Google Cloud service is most appropriate for this task?
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
Cloud Dataflow with fixed windows
Cloud Dataflow is the most appropriate service because it natively supports sliding time windows via the Apache Beam model. While the option mentions 'fixed windows', Cloud Dataflow can also implement sliding windows, which are required for computing aggregate features over overlapping intervals. The other services lack native sliding window aggregation capabilities.
Key principle: Sliding Windows
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cloud Dataflow with fixed windows
Why this is correct
Cloud Dataflow supports sliding windows (e.g., via the SlidingWindows transform in Beam), making it suitable for computing aggregates over overlapping time intervals. The option text says 'fixed windows', but the service itself can handle sliding windows.
Related concept
Sliding Windows
- ✗
Cloud Pub/Sub for windowing logic
Why it's wrong here
Cloud Pub/Sub is a messaging service for streaming data ingestion; it does not provide windowing logic or compute features.
- ✗
BigQuery scheduled queries
Why it's wrong here
BigQuery scheduled queries are batch-oriented and cannot process real-time streaming data with sliding windows.
- ✗
Cloud Functions with Pub/Sub triggers
Why it's wrong here
Cloud Functions with Pub/Sub triggers can react to individual events but lack native support for stateful sliding-window aggregations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may assume that because an option says 'fixed windows', it cannot perform sliding intervals, but Cloud Dataflow actually supports both fixed and sliding windows. The key is to recognize that Dataflow is the appropriate service for windowed stream processing.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Dataflow uses Apache Beam's windowing and trigger mechanisms to assign each event to one or more windows (e.g., fixed windows of 1 minute) and then aggregate per window. For sliding windows, you can use `SlidingWindows` with a specified duration and period, which creates overlapping windows that emit results at each period interval. A real-world scenario is a fraud detection pipeline that computes the average transaction amount over the last 10 minutes, sliding every 1 minute, to detect anomalies in near real time.
KKey Concepts to Remember
- Sliding Windows
- Cloud Dataflow
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
Sliding Windows
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.
Review sliding Windows, then practise related PMLE questions on the same topic to reinforce the concept.
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Scaling Prototypes into ML Models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Sliding Windows.
What is the correct answer to this question?
The correct answer is: Cloud Dataflow with fixed windows — Cloud Dataflow is the most appropriate service because it natively supports sliding time windows via the Apache Beam model. While the option mentions 'fixed windows', Cloud Dataflow can also implement sliding windows, which are required for computing aggregate features over overlapping intervals. The other services lack native sliding window aggregation capabilities.
What should I do if I get this PMLE question wrong?
Review sliding Windows, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Sliding Windows
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Last reviewed: Jul 4, 2026
This PMLE 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 PMLE exam.
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