This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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.
Exhibit
Refer to the exhibit.
```
Deploying a model to Vertex AI Endpoint with GPU:
$ gcloud ai endpoints deploy-model $ENDPOINT_ID \
--model=$MODEL_ID \
--machine-type=n1-standard-4 \
--accelerator=count=1,type=nvidia-tesla-t4 \
--min-replica-count=2 \
--max-replica-count=5
```
The exhibit shows a command to deploy a model to a Vertex AI endpoint with GPU. The deployment fails due to a resource constraint. What is the most likely reason?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Exhibit
Refer to the exhibit.
```
Deploying a model to Vertex AI Endpoint with GPU:
$ gcloud ai endpoints deploy-model $ENDPOINT_ID \
--model=$MODEL_ID \
--machine-type=n1-standard-4 \
--accelerator=count=1,type=nvidia-tesla-t4 \
--min-replica-count=2 \
--max-replica-count=5
```
A
The --model flag points to an autoML model.
Why wrong: AutoML models can be deployed to endpoints.
B
The accelerator type is misspelled.
Why wrong: nvidia-tesla-t4 is correct.
C
The machine type n1-standard-4 does not support GPU accelerators.
n1-standard machines do not have enough PCIe lanes; use n1-highmem or n1-highcpu.
D
The min-replica-count is greater than the max-replica-count.
The answer is that the deployment fails because the machine type n1-standard-4 does not support GPU accelerators. While n1-standard machines can technically attach certain GPUs, the most common constraint in Vertex AI is that GPU compatibility requires specific machine families like n1-highmem or n1-highcpu, which provide the necessary PCIe lanes and power for GPU attachment; n1-standard instances often lack this support in practice, especially for newer GPU types like T4 or V100. On the Google Cloud Generative AI Leader exam, this question tests your understanding of Vertex AI GPU machine type compatibility, a frequent pitfall where candidates assume all n1-series machines can host GPUs. A common trap is confusing regional GPU availability with machine type limitations—always verify the machine family first. Memory tip: “Standard is for CPU, High is for GPU”—if you need a GPU, go with highmem or highcpu.
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
✓
The machine type n1-standard-4 does not support GPU accelerators.
Option C is correct because the n1-standard-4 machine type does not support attaching GPUs. In Vertex AI, GPU accelerators require specific machine series (e.g., n1-highmem-* or n1-highcpu-* for NVIDIA Tesla GPUs, or newer machine families like a2-highgpu-* for A100 GPUs). The n1-standard-4 is a general-purpose machine type that lacks the necessary PCIe lanes and power delivery to accommodate a GPU accelerator, causing a resource constraint failure during deployment.
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.
✗
The --model flag points to an autoML model.
Why it's wrong here
AutoML models can be deployed to endpoints.
✗
The accelerator type is misspelled.
Why it's wrong here
nvidia-tesla-t4 is correct.
✓
The machine type n1-standard-4 does not support GPU accelerators.
Why this is correct
n1-standard machines do not have enough PCIe lanes; use n1-highmem or n1-highcpu.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The min-replica-count is greater than the max-replica-count.
Why it's wrong here
min=2, max=5 is valid.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume any n1-standard machine type can support GPUs, but Google Cloud restricts GPU attachments to specific machine types within the N1 family (highmem/highcpu) and newer families like A2 or G2.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI uses Compute Engine machine types and accelerator configurations. The n1-standard-4 is part of the N1 general-purpose family, which does not support GPU attachments; only N1 high-memory (n1-highmem-*) and high-CPU (n1-highcpu-*) machine types support GPUs. When deploying with a GPU, the system checks the machine type's compatibility with the requested accelerator, and if incompatible, it raises a resource constraint error (e.g., 'The machine type does not support the requested accelerator'). In real-world scenarios, this often catches users who assume any n1 machine type can accept GPUs, leading to failed deployments.
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.
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: The machine type n1-standard-4 does not support GPU accelerators. — Option C is correct because the n1-standard-4 machine type does not support attaching GPUs. In Vertex AI, GPU accelerators require specific machine series (e.g., n1-highmem-* or n1-highcpu-* for NVIDIA Tesla GPUs, or newer machine families like a2-highgpu-* for A100 GPUs). The n1-standard-4 is a general-purpose machine type that lacks the necessary PCIe lanes and power delivery to accommodate a GPU accelerator, causing a resource constraint failure during deployment.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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