- A
Use Dataproc with Spark Streaming to read from Pub/Sub and write to Feature Store
Why wrong: Possible but Dataflow is more integrated with Feature Store and easier to manage.
- B
Use Cloud Functions triggered by Pub/Sub to compute features and update Feature Store
Why wrong: Cloud Functions are short-lived and not designed for complex streaming transformations at scale.
- C
Use Dataflow streaming pipeline with Apache Beam to read from Pub/Sub, compute features, and write to Feature Store
Dataflow streaming is ideal for scalable, low-latency stream processing with exactly-once semantics.
- D
Use Cloud Run to consume Pub/Sub messages and update Feature Store via a service
Why wrong: Cloud Run is stateless and not built for streaming; scaling may cause issues.
Question 538 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.
You need to perform a large-scale feature computation on streaming data from Pub/Sub, transforming raw events into features, and writing results to Vertex AI Feature Store for online serving. Which Google Cloud architecture is most appropriate?
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 Dataflow streaming pipeline with Apache Beam to read from Pub/Sub, compute features, and write to Feature Store
Dataflow with streaming (Apache Beam) can read from Pub/Sub, transform data, and write to Feature Store via the online serving API. Cloud Functions is not suitable for complex transforms. Dataproc Streaming is possible but Dataflow is more natural. Cloud Run is for request-response.
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 Dataproc with Spark Streaming to read from Pub/Sub and write to Feature Store
Why it's wrong here
Possible but Dataflow is more integrated with Feature Store and easier to manage.
- ✗
Use Cloud Functions triggered by Pub/Sub to compute features and update Feature Store
Why it's wrong here
Cloud Functions are short-lived and not designed for complex streaming transformations at scale.
- ✓
Use Dataflow streaming pipeline with Apache Beam to read from Pub/Sub, compute features, and write to Feature Store
Why this is correct
Dataflow streaming is ideal for scalable, low-latency stream processing with exactly-once semantics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Run to consume Pub/Sub messages and update Feature Store via a service
Why it's wrong here
Cloud Run is stateless and not built for streaming; scaling may cause issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Dataflow streaming pipeline with Apache Beam to read from Pub/Sub, compute features, and write to Feature Store — Dataflow with streaming (Apache Beam) can read from Pub/Sub, transform data, and write to Feature Store via the online serving API. Cloud Functions is not suitable for complex transforms. Dataproc Streaming is possible but Dataflow is more natural. Cloud Run is for request-response.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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