- A
n1-standard-4 with 1 NVIDIA Tesla T4
Attaching a GPU to an n1-standard machine enables GPU acceleration.
- B
n1-standard-4
Why wrong: n1-standard is a CPU-only machine type, does not support GPU.
- C
e2-standard-4
Why wrong: e2-series machines do not support GPUs.
- D
n1-highmem-8
Why wrong: n1-highmem is CPU-only, no GPU support.
Question 770 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 data scientist wants to deploy a trained TensorFlow model to Vertex AI for online predictions. They need to serve predictions with low latency and want to leverage GPU acceleration. Which machine type should they select when creating the Vertex AI endpoint?
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
✓
n1-standard-4 with 1 NVIDIA Tesla T4
Option A is correct because the n1-standard-4 machine type supports attaching GPUs such as the NVIDIA Tesla T4, which provides GPU acceleration for low-latency online predictions. Vertex AI endpoints require a machine type that allows GPU attachment, and the n1-series is one of the few families that supports GPUs, while the T4 offers a good balance of cost and performance for inference workloads.
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.
- ✓
n1-standard-4 with 1 NVIDIA Tesla T4
Why this is correct
Attaching a GPU to an n1-standard machine enables GPU acceleration.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
n1-standard-4
Why it's wrong here
n1-standard is a CPU-only machine type, does not support GPU.
- ✗
e2-standard-4
Why it's wrong here
e2-series machines do not support GPUs.
- ✗
n1-highmem-8
Why it's wrong here
n1-highmem is CPU-only, no GPU support.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume any machine type can be paired with a GPU, but only specific series (like n1, n2, g2) support GPU attachment, and the e2 series explicitly does not, leading to a wrong selection if the GPU requirement is overlooked.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use machine types from Compute Engine, and GPU attachment is only supported on certain machine families (n1, n2, g2, etc.). The NVIDIA Tesla T4 is optimized for inference with Tensor Cores and supports mixed-precision (FP16/INT8) to reduce latency. When deploying a TensorFlow model, the T4 can leverage TensorFlow Serving with GPU support, but you must ensure the model is compatible with the GPU and that the serving container includes the necessary CUDA drivers.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Serving and Scaling Models — study guide chapter
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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: n1-standard-4 with 1 NVIDIA Tesla T4 — Option A is correct because the n1-standard-4 machine type supports attaching GPUs such as the NVIDIA Tesla T4, which provides GPU acceleration for low-latency online predictions. Vertex AI endpoints require a machine type that allows GPU attachment, and the n1-series is one of the few families that supports GPUs, while the T4 offers a good balance of cost and performance for inference workloads.
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
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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