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Microsoft AI-300 Exam Syllabus Topics:

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

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q84-Q89):

NEW QUESTION # 84
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?

A. Create the model explanations.
B. Deploy the model to a managed online endpoint.
C. Register the model with the workspace.
D. Convert the model from the MLflow format to a custom format.

Answer: C

Explanation:
The first step you must take is to register the model with the workspace.
To access the no-code, guided wizard for generating a Responsible AI dashboard directly within the Azure Machine Learning studio UI, the trained model must first exist as a recognized asset inside your workspace's model registry.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard

NEW QUESTION # 85
You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible Al dashboard.
Based on what you learn from the dashboard, you must perform the following activities:
- Determine what must be done to get a desirable outcome from the
model.
- Identify the features that have the most direct effect on your
outcome of interest.
You need to select the components to use for the Responsible Al dashboard configuration.
Which two components should you add? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. explanation
B. causal
C. error analysis
D. counterfactuals

Answer: B,D

Explanation:
To meet your requirements, you should configure the Responsible AI (RAI) dashboard with the Counterfactuals (What-If) and Causal Analysis components.
1. Counterfactuals (What-If)
Purpose: This component helps you figure out how to change the model's output to a target result for specific instances.
Mechanism: It generates counterfactual examples (the closest possible data points) that yield a different, desirable outcome. For example, it can answer: "What is the minimum amount this user's income needs to increase for their loan application to be approved?"
2. Causal Analysis
Purpose: This component estimates the direct causal effect of specific "treatment" features on your ultimate outcome of interest.
Mechanism: Unlike standard feature importance (which only shows correlation), causal inference answers deep prescription questions. It separates pure correlation from true causation to tell you exactly how changing a real-world policy or feature directly moves your target metric.
Reference:
https://docs.azure.cn/en-us/machine-learning/concept-responsible-ai-dashboard?view=azureml-api-2

NEW QUESTION # 86
Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

NEW QUESTION # 87
During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?

A. Ignore schema differences
B. Add a data validation component in pipeline
C. Retrain manually when failure occurs
D. Use larger compute

Answer: B

NEW QUESTION # 88
DRAG DROP
A team deploys a classification model to production and scores incoming customer data daily.
After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy.
You need to determine whether data drift is occurring and if it is, identify the appropriate actions.
Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. You may use each action 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.

Answer:

Explanation:

NEW QUESTION # 89
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