AZ-900 Describe Azure architecture and services Practice Question
Which Azure service provides a fully managed, cloud-hosted Kubernetes environment for AI and machine learning workloads?
⚠ Common exam trap
Candidates often confuse Azure Machine Learning compute clusters (which also support GPU VMs) with a managed Kubernetes environment, not realizing that AKS is the dedicated Kubernetes service and that Azure ML compute clusters are not Kubernetes-based.
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
✓
Azure Kubernetes Service with GPU nodes
Azure Kubernetes Service (AKS) with GPU nodes is the correct answer because it provides a fully managed Kubernetes cluster that can be configured with GPU-enabled virtual machines, making it ideal for running AI and machine learning workloads that require accelerated computing. AKS handles the control plane, patching, and scaling, while allowing you to deploy containerized ML models or training jobs using Kubernetes orchestration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Machine Learning compute clusters
Why it's wrong here
Azure Machine Learning compute clusters are a managed pool of VMs that auto-scale for training jobs, but they do not expose a Kubernetes API or allow custom orchestration of containers. While they can run the Azure ML training runtime, they lack the Kubernetes control plane needed to manage complex, non-standard ML deployments. For Kubernetes-based orchestration with GPU support, AKS with GPU-enabled node pools is the required infrastructure.
- ✓
Azure Kubernetes Service with GPU nodes
Why this is correct
Azure Kubernetes Service (AKS) with GPU nodes provides a fully managed Kubernetes control plane and worker nodes equipped with GPUs, enabling efficient scheduling and orchestration of containerized AI/ML workloads. The Kubernetes scheduler, combined with device plugins, can allocate GPUs to pods, while features like cluster autoscaler and horizontal pod autoscaler support dynamic resource scaling. This makes AKS the correct choice for production-grade ML systems that need portability and orchestration at scale.
- ✗
Azure Batch AI
Why it's wrong here
Azure Batch AI was a legacy service introduced for running AI training jobs across a cluster of VMs, but it has been discontinued and is now replaced by Azure Machine Learning and AKS. It did not provide a native Kubernetes API or GPU-aware orchestration as AKS does, and its focus on batch processing differed from the general-purpose container orchestration that AKS offers. Therefore, referencing Batch AI today is outdated and incorrect for a Kubernetes-based ML workload.
- ✗
Azure Neural Network Computing
Why it's wrong here
Azure Neural Network Computing is not a recognized Azure service, so it cannot be used for orchestrating ML workloads. The name resembles concepts like neural processing units, but Azure does not offer a service by this name, nor a standalone neural-network-specific computing platform. To achieve Kubernetes orchestration for ML with GPU scaling, Azure customers should use AKS with GPU-enabled node pools instead of a non-existent service.
Go deeper
Related to this question
Learn chapter
Azure Regions and Geographies
Key term
Kubernetes cluster
A set of machines, called nodes, that work together to run and manage containerized applications using Kubernetes orchestration software.
Key term
Azure Kubernetes Service
Azure Kubernetes Service is a managed container orchestration service that simplifies deploying, managing, and scaling containerized applications using Kubernetes on Microsoft Azure.
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