- A
n1-standard-4 with attached GPU
You need GPU machine type or attach GPU to N1.
- B
e2-standard-4
Why wrong: E2 are cost-optimized, no GPU support.
- C
n1-highmem-8
Why wrong: High memory, no GPU.
- D
n1-standard-4
Why wrong: No GPU; CPU only.
Question 832 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Which machine type is most suitable for a Vertex AI endpoint serving a GPU-accelerated model?
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 attached GPU
Option A is correct because Vertex AI endpoints require a machine type that supports GPU acceleration, and the n1-standard-4 with an attached GPU provides the necessary CPU-to-GPU balance for inference workloads. The n1 series supports GPU attachments via the `accelerator` configuration, enabling CUDA-based model serving, while the e2 series and n1-highmem-8 without GPU cannot leverage GPU acceleration.
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 attached GPU
Why this is correct
You need GPU machine type or attach GPU to N1.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
e2-standard-4
Why it's wrong here
E2 are cost-optimized, no GPU support.
- ✗
n1-highmem-8
Why it's wrong here
High memory, no GPU.
- ✗
n1-standard-4
Why it's wrong here
No GPU; CPU only.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that any n1 machine type inherently supports GPU acceleration, but the trap is that the GPU must be explicitly attached via the `accelerator` configuration. Options like n1-highmem-8 or n1-standard-4 without the `accelerator` are CPU-only, failing to meet the requirement for a GPU-accelerated model on Vertex AI.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use NVIDIA GPUs (e.g., T4, V100, A100) via the `accelerator` field in the machine spec, which requires a compatible machine series like n1, n2, or g2. The n1-standard-4 provides 4 vCPUs and 15 GB of memory, which is a common baseline for serving models with a single GPU, balancing compute and memory for inference. In real-world scenarios, choosing a machine with insufficient CPU cores can bottleneck GPU utilization, while excessive memory (e.g., n1-highmem-8) wastes resources without improving throughput.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 attached GPU — Option A is correct because Vertex AI endpoints require a machine type that supports GPU acceleration, and the n1-standard-4 with an attached GPU provides the necessary CPU-to-GPU balance for inference workloads. The n1 series supports GPU attachments via the `accelerator` configuration, enabling CUDA-based model serving, while the e2 series and n1-highmem-8 without GPU cannot leverage GPU acceleration.
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 →
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Last reviewed: Jul 4, 2026
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