- A
c2-standard-16 with no GPU
Why wrong: No GPU, cannot run generative model efficiently.
- B
a2-highgpu-4g with 4 A100 GPUs
A2 machines offer A100s with large memory, suitable for large models.
- C
n1-standard-4 with a single T4 GPU
Why wrong: T4 GPU has limited memory, not suitable for large models.
- D
n2-standard-8 with a single P4 GPU
Why wrong: P4 is older and lower memory.
Question 121 of 997
Deploy Large Generative Models with A2 High-GPU
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 engineer is deploying a large generative model on Vertex AI. The model requires a GPU with high memory. Which machine configuration should they choose?
Quick Answer
The answer is the a2-highgpu-4g machine with 4 A100 GPUs. This configuration is correct because deploying a large generative model on Vertex AI requires both massive parallel processing and high GPU memory, and the A100’s 40GB or 80GB HBM2e memory per GPU provides the necessary bandwidth to hold model weights and activations without spilling to slower storage. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of machine family specialization—specifically, that A2 high-GPU machines are purpose-built for memory-intensive training and inference, unlike standard compute-optimized instances. A common trap is choosing a lower-GPU count or a non-A2 series, which would cause out-of-memory errors for large models. Remember the mnemonic: “A2 for A100, high-GPU for high memory”—if the model is large, you need the A2’s dedicated high-bandwidth memory architecture.
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
✓
a2-highgpu-4g with 4 A100 GPUs
Option B is correct because the a2-highgpu-4g machine series is specifically designed for large-scale GPU-accelerated workloads, offering 4 NVIDIA A100 GPUs with 40GB of high-bandwidth memory (HBM2e) each, totaling 160GB of GPU memory. This configuration provides the high memory capacity required for training or serving large generative models, such as LLMs or diffusion models, which often exceed the memory limits of smaller GPUs.
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.
- ✗
c2-standard-16 with no GPU
Why it's wrong here
No GPU, cannot run generative model efficiently.
- ✓
a2-highgpu-4g with 4 A100 GPUs
Why this is correct
A2 machines offer A100s with large memory, suitable for large models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
n1-standard-4 with a single T4 GPU
Why it's wrong here
T4 GPU has limited memory, not suitable for large models.
- ✗
n2-standard-8 with a single P4 GPU
Why it's wrong here
P4 is older and lower memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may choose a cheaper or single-GPU option (like C or D) without calculating the total GPU memory needed, or mistakenly think a CPU-only instance (A) can handle GPU-accelerated workloads, ignoring that large generative models require both high GPU memory and parallel processing.
Detailed technical explanation
How to think about this question
The a2-highgpu-4g uses NVIDIA A100 GPUs with NVLink and NVSwitch interconnects, enabling high-bandwidth communication between GPUs for model parallelism. In practice, large generative models like GPT-3 (175B parameters) require multiple A100 GPUs to fit in memory, and the a2-highgpu-4g provides a balanced ratio of GPU memory to compute for fine-tuning or inference. The 'g' suffix indicates the GPU memory is optimized for large models, with 40GB per GPU compared to the standard 80GB variant, making it cost-effective for models that fit within 160GB total.
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
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: a2-highgpu-4g with 4 A100 GPUs — Option B is correct because the a2-highgpu-4g machine series is specifically designed for large-scale GPU-accelerated workloads, offering 4 NVIDIA A100 GPUs with 40GB of high-bandwidth memory (HBM2e) each, totaling 160GB of GPU memory. This configuration provides the high memory capacity required for training or serving large generative models, such as LLMs or diffusion models, which often exceed the memory limits of smaller GPUs.
What should I do if I get this Generative AI Leader 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 Generative AI Leader 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 Generative AI Leader exam.
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