- A
Cloud Dataflow with fixed windows
Dataflow allows windowed aggregations (sliding, fixed, session) on streaming data.
- B
Cloud Pub/Sub for windowing logic
Why wrong: Pub/Sub is a messaging system, not a compute engine for windowing.
- C
BigQuery scheduled queries
Why wrong: Scheduled queries run periodically on batch data, not on streaming.
- D
Cloud Functions with Pub/Sub triggers
Why wrong: Cloud Functions is not designed for sliding window aggregations.
Question 888 of 1,000
PMLE Scaling Prototypes into ML Models 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. 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 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 with fixed windows is the most appropriate choice because it natively supports windowing and aggregation over streaming data using the Apache Beam programming model. Fixed windows allow you to define sliding time intervals (e.g., every 5 minutes) to compute aggregate features like sums or averages, which is exactly what the feature pipeline requires.
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.
- ✓
Cloud Dataflow with fixed windows
Why this is correct
Dataflow allows windowed aggregations (sliding, fixed, session) on streaming data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Pub/Sub for windowing logic
Why it's wrong here
Pub/Sub is a messaging system, not a compute engine for windowing.
- ✗
BigQuery scheduled queries
Why it's wrong here
Scheduled queries run periodically on batch data, not on streaming.
- ✗
Cloud Functions with Pub/Sub triggers
Why it's wrong here
Cloud Functions is not designed for sliding window aggregations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Cloud Pub/Sub's ability to handle streaming data with the ability to perform windowed aggregations, but Pub/Sub is only a transport layer and cannot compute features itself.
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
- 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
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Cloud Dataflow with fixed windows — Cloud Dataflow with fixed windows is the most appropriate choice because it natively supports windowing and aggregation over streaming data using the Apache Beam programming model. Fixed windows allow you to define sliding time intervals (e.g., every 5 minutes) to compute aggregate features like sums or averages, which is exactly what the feature pipeline requires.
What should I do if I get this PMLE 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: 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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