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AI-300 Operationalizing Machine Learning and Generative AI Solutions (beta)

  • Microsoft
  • 116 practice questions
  • Updated Aug 2026
  • 5 free sample questions

About this exam

AI-300 is the Microsoft exam Operationalizing Machine Learning and Generative AI Solutions (beta). Practice material is available as a PDF, an interactive practice test or a bundle of both.

Exam code
AI-300
Vendor
Microsoft
Practice questions
116
Questions with an explanation
116 of 116
Free sample questions
5
Question bank last updated
3 Aug 2026

Try 2 sample questions

Taken from the free sample set for AI-300.

Question 1

You manage an Azure Machine learning workspace. You develop a machine learning model. You must deploy the model to use a low-priority VM with a pricing discount. You need to deploy the model. Which compute target should you use? 

  1. Option A:
    Azure Container Instances (ACI)
  2. Option B:
    Azure Machine Learning compute clusters
  3. Option C:
    Local deployment
  4. Option D:
    Azure Kubernetes Service (AKS) 
Show answer and explanation

Correct answer: B

B. Azure Machine Learning compute clusters is the correct answer because Azure Machine Learning Compute Clusters support low-priority (Spot) virtual machines, which provide significant cost savings compared to standard VMs. These clusters are designed for machine learning workloads and can automatically scale based on demand. In contrast, Azure Container Instances (ACI) and local deployments do not support low-priority VMs, while Azure Kubernetes Service (AKS) is primarily used for production inference deployments and is not the typical compute target for leveraging low-priority VMs in Azure Machine Learning. Therefore, if the goal is to deploy a machine learning model using discounted low-priority compute resources, Azure Machine Learning Compute Clusters are the best choice.

Question 2

You need to recommend an experiment-tracking strategy that ensures consistent experiment results. What should you recommend?

  1. Option A:
    Azure Machine Learning job output logs
  2. Option B:
    MLflow experiment tracking
  3. Option C:
    Application Insights logs
  4. Option D:
    Azure Monitor alerts 
Show answer and explanation

Correct answer: B

MLflow provides a structured and consistent way to track machine learning experiments, including parameters, metrics, artifacts, and model versions. By using MLflow experiment tracking within Azure Machine Learning, you ensure that every run is logged in a standardized format, making results reproducible and comparable across different experiments. It also helps maintain consistency by storing all experiment metadata centrally, so you can easily reproduce a model’s performance and verify results over time. Unlike general logging or monitoring tools, MLflow is specifically designed for experiment lifecycle management, which makes it the best choice for ensuring consistent experiment tracking in machine learning workflows.
See all 5 free sample questions