The correct answer is to deploy the container to a container group with a GPU-enabled SKU, such as the NV series. This is required because Azure Container Instances exposes GPU hardware only through specific VM-sized SKUs like Standard_NC6s_v3, which include NVIDIA Tesla GPUs (e.g., K80, P100, or V100) that are directly mapped to the container for hardware-accelerated machine learning inference. On the AZ-204 exam, this scenario tests your understanding of resource-level provisioning for specialized workloads—specifically, that GPU support in ACI is not automatic and must be explicitly requested via the SKU in the container group’s resource configuration. A common trap is assuming you can simply add a GPU flag to any standard container group; in reality, the entire group must be deployed on a GPU-optimized SKU, and your container image must include the NVIDIA CUDA runtime. Memory tip: think “NV SKU = NVIDIA Virtual GPU” to remember that the SKU name directly indicates GPU capability.
AZ-204 Develop Azure compute solutions Practice Question
This AZ-204 practice question tests your understanding of develop azure compute solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are deploying a Docker container to Azure Container Instances (ACI). The container must use GPU resources for machine learning inference. You need to select the appropriate option to provision GPU-enabled containers. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy the container to a container group with a GPU-enabled SKU (e.g., NV series).
Azure Container Instances supports GPU resources only when you deploy a container group using a GPU-optimized SKU, such as the NV-series (e.g., Standard_NC6s_v3). These SKUs provide NVIDIA Tesla GPUs (e.g., K80, P100, V100) that are directly exposed to the container, enabling hardware-accelerated machine learning inference. You must specify the GPU SKU in the container group's resource requests during deployment, and the container image must include the appropriate NVIDIA CUDA drivers or runtime.
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.
✓
Deploy the container to a container group with a GPU-enabled SKU (e.g., NV series).
Why this is correct
A GPU SKU in the container group resource allocation assigns a physical GPU from the Azure infrastructure to the container instance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Mount a GPU volume from the host.
Why it's wrong here
ACI does not support GPU volumes; GPUs are allocated via the container group SKU, not by mounting host devices.
✗
Use Azure Batch with GPU-enabled pools.
Why it's wrong here
Azure Batch is an alternative service for GPU workloads but is not part of ACI; the question specifically asks about ACI.
✗
Enable container GPU support in the Dockerfile.
Why it's wrong here
The Dockerfile can request GPU access but the underlying infrastructure must provide it; ACI requires selecting a GPU SKU to supply GPUs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse local Docker GPU configuration (e.g., `--gpus all` in Dockerfile or docker run) with ACI's infrastructure-level GPU provisioning, assuming that a Dockerfile directive alone will enable GPU access in ACI, when in fact the SKU selection is mandatory and overrides any local settings.
Detailed technical explanation
How to think about this question
Under the hood, ACI maps the GPU SKU to a specific NVIDIA GPU model (e.g., Tesla K80 for Standard_NC6) and exposes it via the NVIDIA Container Toolkit, which mounts the GPU device and CUDA libraries into the container. A subtle behavior is that the container image must be compatible with the GPU driver version installed on the ACI host (e.g., CUDA 11.x for certain SKUs), or you may need to use a base image like `nvidia/cuda:11.0-runtime`. In a real-world scenario, if you deploy a PyTorch inference container without the correct CUDA runtime, the container will fall back to CPU, negating the GPU benefit.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-204 question in full detail.
Develop Azure compute solutions — This question tests Develop Azure compute solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy the container to a container group with a GPU-enabled SKU (e.g., NV series). — Azure Container Instances supports GPU resources only when you deploy a container group using a GPU-optimized SKU, such as the NV-series (e.g., Standard_NC6s_v3). These SKUs provide NVIDIA Tesla GPUs (e.g., K80, P100, V100) that are directly exposed to the container, enabling hardware-accelerated machine learning inference. You must specify the GPU SKU in the container group's resource requests during deployment, and the container image must include the appropriate NVIDIA CUDA drivers or runtime.
What should I do if I get this AZ-204 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.
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Question Discussion
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