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Microsoft AI-300 Exam Syllabus Topics:

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

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A team provisions an Azure Machine Learning environment by triggering pull requests.
Deployments must be automated, auditable, and require approval before running.
You need to select a deployment automation tool.
Which tool should you use?

A) Azure Machine Learning pipelines
B) GitHub Actions
C) MLflow
D) Azure Monitor

2. Hotspot Question
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. A team manages prompts used by a generative AI application built on Microsoft Foundry. Prompt updates are frequent, and prior changes have caused unexpected behavior in production.
The team requires that new prompt versions can be tested and reviewed without affecting production behavior.
You need to implement a source control practice that allows safe prompt experimentation.
What should you do?

A) Create a feature git branch for each prompt change.
B) Revert the prompt in the production environment to a previous commit.
C) Create a GitHub Action workflow.

4. A data science team registers an MLflow model in Azure Machine Learning.
The model must support low latency predictions and automatically scale based on incoming request volume.
You need to deploy the model.
Which deployment option should you use?

A) Managed online endpoint
B) Server less endpoint
C) Azure OpenAI endpoint
D) Batch endpoint

5. Hotspot Question
You have an Azure Machine Learning workspace named Workspace1.
You plan to train an image classification model by using Automated ML in Workspace1.
You need to complete the provided Azure Machine Learning Python SDK v2 code to bring labeled image data as input for model training.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.