- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Sep 18, 2026
- Q & A: 189 Questions and Answers
Microsoft AI-300 Exam : Operationalizing Machine Learning and Generative AI Solutions
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - Large language model integration
|
| Topic 2: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 3: Implement secure and scalable AI systems | - Security and governance
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question #1
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
Stakeholders must be notified of the drops.
Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Microsoft ' s guidance on production model monitoring prescribes a four-stage sequence. First, configure an Azure ML model monitor on the deployed endpoint, specifying which metrics to track and on what schedule.
Second, define the threshold value below which model performance is considered unacceptable - this becomes the trigger condition. Third, create an Azure Monitor alert rule that evaluates the monitored metric against the threshold and fires when it is breached; the alert rule is associated with an action group that sends notifications to stakeholders via email, SMS, or Teams webhook. Fourth, the action group includes a webhook action pointing to an Azure Machine Learning pipeline ' s published REST endpoint, which starts the retraining job automatically. This sequence cleanly separates detection, notification, and remediation, matching Microsoft ' s recommended MLOps automation pattern.
Microsoft Learn Reference Topic: Automate model retraining based on monitoring alerts - Azure Machine Learning MLOps
Question #2
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
For tracking changes across contributors, Git integration is the answer: by connecting the Microsoft Foundry project to a Git repository, every prompt file change is tracked as a commit with author attribution, timestamp, and diff view, and pull requests enforce review before changes reach production. The Git history provides the complete audit trail and rollback capability needed for traceability. For allowing applications to consume updated prompts without requiring redeployment, Microsoft Foundry ' s prompt management feature allows prompts to be stored and versioned as named artifacts in the project. Applications reference prompts by name and load the latest approved version at inference time, rather than having prompt text hard-coded in the application deployment artifact. This decoupling means updating a prompt is a content operation - not a code deployment - so applications automatically pick up the new prompt without any redeployment.
Microsoft Learn Reference Topic: Prompt management in Microsoft Azure AI Foundry - Git integration and dynamic prompt versioning
Question #3
You manage an Azure Machine Learning workspace by using the Azure CLI ml extension v2. You need to define a YAML schema to create a compute cluster. Which schema should you use?
- A. https://azuremlschemas.azureedge.net/latest/vmCompute.schema.json
- B. https://azuremlschemas.azureedge.net/latest/kubernetesCompute.schema.json
- C. https://azuremlschemas.azureedge.net/latest/computdnstarKeichema.json
- D. https://azuremlschemas.azureedge.net/latest/amlCompute.schemajson
Correct Answer: D 🗳️
Question #4
You create an MLflow model
You must deploy the model to Azure Machine Learning for batch inference.
You need to create the batch deployment.
Which two components should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point
- A. Online endpoint
- B. Model files
- C. Environment
- D. Kubernetes online endpoint
- E. Compute target
Correct Answer: B,E 🗳️
Question #5
You manage an Azure Machine learning workspace named workspace1.
You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
You need to add the instantiated compute instance to workspace 1.
What should you use?
- A. set resources method of an instance of the azureai.ml. Command class
- B. contractor of the azure.ai.ml. mLComputeSchedule class
- C. begin create or update method of a stance of the azure.ai. imLMLCSentdass
- D. constructor of the azure ai.ml. ComputerPowerAction enemy
Correct Answer: C 🗳️
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