Quick Answer
The answer is Azure Machine Learning managed online endpoints. This service is the correct choice because it provides fully managed, autoscaling endpoints built for real-time inference and natively supports custom containers, allowing you to deploy any model packaged as a Docker image without managing the underlying compute infrastructure. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your ability to differentiate between Azure ML’s managed online endpoints and other deployment options like batch endpoints or Azure Kubernetes Service—a common trap is choosing AKS for custom containers, but the key distinction is that managed online endpoints handle autoscaling, health checks, and traffic splitting automatically, while AKS requires manual cluster management. Remember the memory tip: “Managed means no manual Kubernetes”—if the requirement says “managed endpoints with autoscaling and custom containers,” think Azure ML managed online endpoints first.
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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.
Your organization is migrating on-premises machine learning models to Azure. The models are used for real-time inference. You need to choose a service that provides managed endpoints with autoscaling and supports custom containers. Which service should you use?
Azure Machine Learning managed online endpoints
Supports custom containers, autoscaling, and managed infrastructure.
Azure Functions
Why wrong: Not designed for ML model serving.
Azure Kubernetes Service (AKS) with manual scaling
Why wrong: Requires more operational overhead.
Azure AI Services custom vision
Why wrong: Only for vision scenarios, not general models.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Machine Learning managed online endpoints
Azure Machine Learning managed online endpoints are the correct choice because they provide fully managed, autoscaling endpoints specifically designed for real-time inference. They support custom container images, allowing you to deploy any model packaged as a Docker container, and handle traffic splitting, health checks, and scaling automatically without managing underlying infrastructure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Machine Learning managed online endpoints
Why this is correct
Supports custom containers, autoscaling, and managed infrastructure.
Related concept
Read the scenario before looking for a memorised answer.
Azure Functions
Why it's wrong here
Not designed for ML model serving.
Azure Kubernetes Service (AKS) with manual scaling
Why it's wrong here
Requires more operational overhead.
Azure AI Services custom vision
Why it's wrong here
Only for vision scenarios, not general models.
Common exam traps
The trap here is that candidates often confuse Azure Kubernetes Service (AKS) as the only option for custom containers, overlooking that Azure Machine Learning managed endpoints natively support custom containers with autoscaling, eliminating the operational burden of managing a Kubernetes cluster.
Trap categories for this question
Scenario analysis trap
Only for vision scenarios, not general models.
Detailed technical explanation
Azure Machine Learning managed online endpoints use a deployment controller that automatically scales the number of pod replicas based on metrics like CPU utilization or request latency, with a default cooldown period of 5 minutes to avoid thrashing. Under the hood, each endpoint routes traffic to a specific deployment using a load balancer, and you can deploy custom containers by specifying an image in a Docker registry (e.g., ACR) with a scoring script that implements the `init()` and `run()` methods. A real-world scenario is deploying a PyTorch model with GPU inference—managed endpoints support GPU SKUs and automatically scale to zero when idle, reducing costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Machine Learning managed online endpoints — Azure Machine Learning managed online endpoints are the correct choice because they provide fully managed, autoscaling endpoints specifically designed for real-time inference. They support custom container images, allowing you to deploy any model packaged as a Docker container, and handle traffic splitting, health checks, and scaling automatically without managing underlying infrastructure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This AI-102 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-102 exam.
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