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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement generative AI quality assurance and observability	10–15%	- Monitor generative AI systems 1. Implement logging and alerting 2. Track usage, performance, and errors - Evaluate and test generative AI applications 1. Define evaluation metrics and criteria 2. Test for safety, accuracy, and relevance
Topic 2: Optimize generative AI systems and model performance	15–20%	- Improve efficiency and cost-effectiveness 1. Manage resource utilization 2. Optimize inference and deployment - Optimize model selection and configuration 1. Tune prompts and generation settings 2. Choose appropriate models and parameters
Topic 3: Design and implement an MLOps infrastructure	15–20%	- Implement infrastructure as code for Machine Learning 1. Use Bicep or Azure CLI to deploy resources 2. Automate infrastructure provisioning - Create and manage Machine Learning workspace resources and assets 1. Configure workspace settings and security 2. Manage compute targets, datastores, and environments
Topic 4: Implement machine learning model lifecycle and operations	25–30%	- Deploy models to production 1. Deploy to real-time and batch endpoints 2. Configure deployment options and scaling - Orchestrate model training and experimentation 1. Create and manage pipelines 2. Track experiments and metrics - Monitor and maintain models in production 1. Implement retraining and update workflows 2. Monitor data and model drift - Register, version, and package models 1. Manage model registry 2. Create reusable model packages
Topic 5: Design and implement a GenAIOps infrastructure	20–25%	- Set up Microsoft Foundry environment 1. Manage compute and deployment resources 2. Configure projects, connections, and security - Implement infrastructure for generative AI workloads 1. Design scalable and secure architecture 2. Integrate with Azure services and tools

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q89-Q94):

NEW QUESTION # 89
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

A. Create a shared Azure Machine Learning environment that includes the asset.
B. Publish the asset as a pipeline component.
C. Publish the asset to an Azure Machine Learning registry.
D. Enable workspace-level Git integration and sync assets between repositories.

Answer: C

NEW QUESTION # 90
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:

You need to complete the Python code to log the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 91
You use Azure Machine Learning to tram a model.
You must use Baylean sampling to Tune hyperparaters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE Each correct selection is worth one point.

A. Uniform
B. QNormal
C. Choice
D. LogUniform
E. Normal

Answer: A,C

NEW QUESTION # 92
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a datastore in the workspace.
Does the solution meet the goal?

A. Yes
B. No

Answer: B

NEW QUESTION # 93
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

A. Create a shared Azure Machine Learning environment that includes the asset.
B. Publish the asset as a pipeline component.
C. Publish the asset to an Azure Machine Learning registry.
D. Enable workspace-level Git integration and sync assets between repositories.

Answer: C

Explanation:
To share validated assets between development and production workspaces while maintaining centralized governance and version control, you should use Azure Machine Learning Registries.
Microsoft Learn
Unlike standard workspaces, registries are workspace-agnostic. They act as a central catalog that decouples assets from individual environments, allowing you to "promote" a model or environment from Dev to Prod without manual duplication or data drift.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries- mlops

NEW QUESTION # 94
......

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