- A
Select a GPU-enabled machine type such as n1-standard-4 with 1 x NVIDIA Tesla T4.
GPU-enabled machine type is necessary for GPU inference.
- B
Enable Vertex AI Model Optimization for automatic GPU compilation.
Why wrong: Model Optimization is optional; not a requirement for GPU deployment.
- C
Deploy the model using a custom container that includes CUDA and cuDNN.
Custom container must have GPU drivers; Vertex AI pre-built containers for common frameworks may include them.
- D
Increase the minimum replicas to at least 2 for GPU redundancy.
Why wrong: Replica count is independent of GPU support; not a requirement.
- E
Use gRPC protocol for prediction requests to reduce latency.
Why wrong: gRPC is supported but not required for GPU deployment.
Question 195 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 deploying a complex model that requires GPU for inference. They want to use Vertex AI for serving. Which TWO steps are required to deploy the model with GPU support? (Choose 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
✓
Select a GPU-enabled machine type such as n1-standard-4 with 1 x NVIDIA Tesla T4.
Option A is correct because Vertex AI requires selecting a GPU-enabled machine type (e.g., n1-standard-4 with 1 x NVIDIA Tesla T4) when deploying a model for inference. This is done in the machine specification of the endpoint deployment, ensuring the GPU hardware is allocated for the serving container.
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.
- ✓
Select a GPU-enabled machine type such as n1-standard-4 with 1 x NVIDIA Tesla T4.
Why this is correct
GPU-enabled machine type is necessary for GPU inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Vertex AI Model Optimization for automatic GPU compilation.
Why it's wrong here
Model Optimization is optional; not a requirement for GPU deployment.
- ✓
Deploy the model using a custom container that includes CUDA and cuDNN.
Why this is correct
Custom container must have GPU drivers; Vertex AI pre-built containers for common frameworks may include them.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the minimum replicas to at least 2 for GPU redundancy.
Why it's wrong here
Replica count is independent of GPU support; not a requirement.
- ✗
Use gRPC protocol for prediction requests to reduce latency.
Why it's wrong here
gRPC is supported but not required for GPU deployment.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that GPU support is automatic or requires only a machine type selection, but the custom container with CUDA/cuDNN is equally mandatory to enable GPU acceleration.
Detailed technical explanation
How to think about this question
When deploying a model with GPU on Vertex AI, the custom container must include CUDA and cuDNN libraries (Option C) to interface with the NVIDIA GPU drivers on the host. The GPU-enabled machine type (Option A) provides the physical GPU, while the container ensures the model can leverage it via CUDA runtime calls. A common real-world scenario is deploying a large language model (LLM) where GPU memory (e.g., 16 GB on T4) is critical for batch inference; without the correct container, the GPU remains idle.
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
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.
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.
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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: Select a GPU-enabled machine type such as n1-standard-4 with 1 x NVIDIA Tesla T4. — Option A is correct because Vertex AI requires selecting a GPU-enabled machine type (e.g., n1-standard-4 with 1 x NVIDIA Tesla T4) when deploying a model for inference. This is done in the machine specification of the endpoint deployment, ensuring the GPU hardware is allocated for the serving container.
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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