- A
Cloud Dataflow for stream processing and feature computation
Dataflow can compute features in near real-time and write to Feature Store.
- B
Cloud Pub/Sub for event ingestion
Pub/Sub provides scalable, low-latency ingestion for streaming events.
- C
Cloud Storage for feature store
Why wrong: Cloud Storage is not a feature store; Vertex AI Feature Store is used.
- D
Cloud Functions for feature transformation
Why wrong: Cloud Functions is not designed for complex stream processing or windowing.
- E
BigQuery for feature storage
Why wrong: BigQuery is not for low-latency online serving; Feature Store is.
Question 293 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. 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 machine learning team is building a feature engineering pipeline using Dataflow. They need to compute features from streaming data and store them in Vertex AI Feature Store for online serving. The features must be updated within 5 seconds of the event. Which TWO services should they combine? (Select 2)
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 for stream processing and feature computation
Cloud Dataflow is correct because it provides unified stream and batch processing with exactly-once semantics, enabling low-latency feature computation from streaming data. It integrates natively with Vertex AI Feature Store for online serving, ensuring features are updated within the required 5-second SLA.
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 for stream processing and feature computation
Why this is correct
Dataflow can compute features in near real-time and write to Feature Store.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Cloud Pub/Sub for event ingestion
Why this is correct
Pub/Sub provides scalable, low-latency ingestion for streaming events.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Storage for feature store
Why it's wrong here
Cloud Storage is not a feature store; Vertex AI Feature Store is used.
- ✗
Cloud Functions for feature transformation
Why it's wrong here
Cloud Functions is not designed for complex stream processing or windowing.
- ✗
BigQuery for feature storage
Why it's wrong here
BigQuery is not for low-latency online serving; Feature Store is.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The exam often tests the distinction between general-purpose storage services (Cloud Storage, BigQuery) and the dedicated online feature store (Vertex AI Feature Store) required for real-time ML serving, leading candidates to pick a storage option instead of the correct streaming ingestion (Pub/Sub) and processing (Dataflow) pair.
Detailed technical explanation
How to think about this question
Dataflow uses the Apache Beam SDK to process unbounded data streams with watermarks and triggers, allowing feature computation within a 5-second window. Pub/Sub provides at-least-once delivery with configurable acknowledgment deadlines, ensuring events are ingested reliably even under high throughput. In practice, the team would configure a Dataflow pipeline with a 5-second processing time trigger and write features to Vertex AI Feature Store's online store (backed by Cloud Bigtable) for low-latency serving.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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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 for stream processing and feature computation — Cloud Dataflow is correct because it provides unified stream and batch processing with exactly-once semantics, enabling low-latency feature computation from streaming data. It integrates natively with Vertex AI Feature Store for online serving, ensuring features are updated within the required 5-second SLA.
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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