- A
Use Cloud Run for preprocessing and send HTTP requests to a GPU-backed Vertex AI endpoint for inference.
Why wrong: Introduces network latency and additional cost.
- B
Use two separate Vertex AI endpoints: one CPU-based for preprocessing, one GPU-based for inference, and chain them with Cloud Tasks.
Why wrong: Adds latency and complexity.
- C
Use Dataflow for preprocessing and then invoke the model, but Dataflow is not designed for real-time prediction.
Why wrong: Dataflow is for batch processing, not online prediction.
- D
Use a single GPU machine (e.g., n1-standard-4 with T4) and perform both preprocessing and inference on the same instance.
This minimizes latency by keeping all processing local, and you can choose a machine with sufficient CPU cores.
Question 412 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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 company is using Vertex AI Prediction with a custom container that performs preprocessing before inference. The preprocessing step is CPU-intensive and the inference step uses a GPU. They want to minimize prediction latency while optimizing cost. Which architecture should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 a single GPU machine (e.g., n1-standard-4 with T4) and perform both preprocessing and inference on the same instance.
Using a CPU-only node for preprocessing and then sending the preprocessed data to a GPU node for inference separates concerns and allows independent scaling, but adds network latency. The best approach is to use a single machine with both CPU and GPU to avoid network round-trip, and to adjust the machine type to have enough CPU resources.
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 Run for preprocessing and send HTTP requests to a GPU-backed Vertex AI endpoint for inference.
Why it's wrong here
Introduces network latency and additional cost.
- ✗
Use two separate Vertex AI endpoints: one CPU-based for preprocessing, one GPU-based for inference, and chain them with Cloud Tasks.
Why it's wrong here
Adds latency and complexity.
- ✗
Use Dataflow for preprocessing and then invoke the model, but Dataflow is not designed for real-time prediction.
Why it's wrong here
Dataflow is for batch processing, not online prediction.
- ✓
Use a single GPU machine (e.g., n1-standard-4 with T4) and perform both preprocessing and inference on the same instance.
Why this is correct
This minimizes latency by keeping all processing local, and you can choose a machine with sufficient CPU cores.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
Serving and Scaling Models — study guide chapter
Learn the concepts, then practise the questions
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Serving and Scaling Models practice questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a single GPU machine (e.g., n1-standard-4 with T4) and perform both preprocessing and inference on the same instance. — Using a CPU-only node for preprocessing and then sending the preprocessed data to a GPU node for inference separates concerns and allows independent scaling, but adds network latency. The best approach is to use a single machine with both CPU and GPU to avoid network round-trip, and to adjust the machine type to have enough CPU resources.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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