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Microsoft Operationalizing Machine Learning and Generative AI Solutions : AI-300
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Oct 14, 2026
- Q & A: 189 Questions and Answers
About Microsoft Operationalizing Machine Learning and Generative AI Solutions : AI-300 Exam
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 2: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 3: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 4: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 5: Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question #1
Fabrikam Inc. needs to improve the performance of a GPT-5 model based on the stated technical requirements.
Which action should you perform first?
- A. Deploy the model to production to gather real-world feedback.
- B. Fine-tune the model to improve accuracy.
- C. Evaluate the model output.
- D. Generate synthetic interaction data.
Correct Answer: C 🗳️
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Question #2
-
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Data asset type: mltable
Scheme of the URL paths to storage locations: wasbs
The correct data asset type is mltable because the image collection is distributed across multiple storage locations . Microsoft identifies MLTable as the appropriate Azure Machine Learning data asset type when unstructured data such as images, text, or audio is spread across multiple storage locations. An MLTable definition can contain multiple file, folder, or pattern entries, allowing one logical data asset to reference image data residing in different storage accounts.
A uri_folder data asset is appropriate when the required unstructured files reside within a single folder path .
It represents one folder location and therefore does not provide the same multi-path abstraction required by this scenario. uri_file represents only an individual file and is clearly unsuitable for a collection of images.
For the path scheme, Azure Machine Learning uses wasbs:// for Azure Blob Storage. Microsoft documents the canonical format as:
wasbs:// < container > @ < account > .blob.core.windows.net/ < path >
By comparison, abfss:// is used for Azure Data Lake Storage Gen2, while azureml:// identifies Azure Machine Learning datastore paths rather than direct Azure Blob Storage URLs.
Therefore, an MLTable containing multiple wasbs:// paths correctly represents image files distributed across the two Azure Blob Storage accounts.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning data assets, MLTable, multi-location datasets, storage URI schemes, and Blob Storage integration.
Question #3
You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a model in the workspace and deploy the model to the target environment.
All new versions of the model registered in the workspace must automatically be deployed to the target environment.
You need to configure Azure Pipelines to deploy the model.
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:
Step 1: Create an Azure DevOps project
Step 2: Create a release pipeline
Sign in to your Azure DevOps organization and navigate to your project.
Go to Pipelines, and then select New pipeline.
Step 3: Install the Machine Learning extension for Azure Pipelines
You must install and configure the Azure CLI and ML extension.
Step 4: Create a service connection
How to set up your service connection
Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.
Note: How to enable model triggering in a release pipeline
Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace.
Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.
Reference:
https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml
https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning
Question #4
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
An Azure AI Hub is the top-level governance container in Microsoft Foundry: it holds shared connections to Azure OpenAI, Azure AI Search, Azure Storage, and other services; it defines network isolation policies; it manages billing and quota at the organizational level. Multiple teams share these resources without each team needing to configure their own connections or negotiate quota independently. An Azure AI Project sits inside the Hub and provides team-level isolation: each project has its own experiments, deployments, prompt flows, evaluations, and fine-tuning jobs, all governed by the Hub ' s shared infrastructure. Different teams get their own project with independent access controls via RBAC, while the platform team manages the shared Hub.
This pattern eliminates redundant resource configurations across teams while maintaining clear team-level boundaries - the correct structure for centralized governance with team isolation.
Microsoft Learn Reference Topic: Microsoft Azure AI Foundry hub and project architecture - Centralized governance and team isolation
Question #5
You create an Azure Machine Learning workspace
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
How should you complete ten code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Correct Answer:
Explanation:
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