AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What does Azure Machine Learning's 'compute cluster' provide?
⚠ Common exam trap
Watch out — candidates often confuse compute cluster (for training) with inference clusters like AKS (for deploying models as REST APIs), leading candidates to select Option A incorrectly.
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
✓
Scalable, auto-scaling cloud compute for running ML training jobs that scales to zero when idle
Azure Machine Learning's compute cluster provides a scalable, auto-scaling cloud compute environment specifically designed for running ML training jobs. It automatically scales up to handle large workloads and scales down to zero nodes when idle, optimizing cost and resource utilization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Kubernetes cluster for deploying trained models as REST APIs
Why it's wrong here
This describes Azure Kubernetes Service (AKS) or Azure ML's managed Kubernetes endpoints, which host trained models for real-time REST API inference. In contrast, an Azure Machine Learning compute cluster is a pool of virtual machines specifically allocated to run training or batch scoring jobs. Deploying models as REST APIs uses inference clusters or AKS, not the training-oriented compute cluster.
- ✓
Scalable, auto-scaling cloud compute for running ML training jobs that scales to zero when idle
Why this is correct
This is correct because an Azure Machine Learning compute cluster is a managed pool of CPU/GPU VMs that auto-scales from zero nodes to the required number for training jobs, then returns to zero when idle. You only pay for the compute while it is actively running, making it cost-efficient for periodic training workloads. It supports parallel training and batch inference, aligning with the description of scalable, auto-scaling cloud compute for ML training.
- ✗
A data storage cluster for distributing training datasets across nodes
Why it's wrong here
A compute cluster is not for data storage or dataset distribution; datasets live in Azure Blob Storage, Data Lake Storage, or registered datastores and are mounted to cluster nodes at run time. The cluster's VMs provide ephemeral compute for executing training scripts, and any local storage on nodes is temporary. Managing distributed training data is a job for Azure Storage and data orchestration services, not the compute cluster itself.
- ✗
A network of IoT sensors for collecting training data
Why it's wrong here
IoT sensors collect telemetry and send it to services like Azure IoT Hub, Event Hubs, or Time Series Insights, which is a data ingestion path, not model training. An Azure ML compute cluster is a cloud-based batch computing resource that runs PyTorch/TensorFlow training jobs; it has no role in capturing physical sensor data. Mixing the edge data collection layer with the cloud compute execution layer confuses the architecture.
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Types of AI Workloads
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
Key term
Azure Machine Learning
Azure Machine Learning is a cloud service for building, training, and deploying machine learning models at scale.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.