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Microsoft AI-300 Exam Syllabus Topics:

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

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Hotspot Question
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. Hotspot Question
You are using hyperparameter tuning in Azure Machine Learning Python SDK v2 to train a model.
You configure the hyperparameter tuning experiment by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

3. You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?

A) JSONL
B) CSV
C) TSV
D) JSON

4. You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
- accounts for the performance of all previous runs when evaluating the current run
- avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Truncation selection
B) Bandit
C) Median stopping
D) Default

5. 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.