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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: 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. Define evaluation metrics and criteria 2. Test for safety, accuracy, and relevance
Topic 3: 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. Track experiments and metrics 2. Create and manage pipelines - Deploy models to production 1. Configure deployment options and scaling 2. Deploy to real-time and batch endpoints - Monitor and maintain models in production 1. Monitor data and model drift 2. Implement retraining and update workflows
Topic 4: 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
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 (Q104-Q109):

NEW QUESTION # 104
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?

A. Apply a blocklist.
B. Configure content filters.
C. Generate synthetic interaction data.
D. Enable observability metrics.

Answer: C

Explanation:
In Microsoft Foundry, you can test and compare LLM prompt variants using Prompt Flow. This environment allows you to create a controlled development workflow with consistent inputs, bypassing the need for live user traffic.
1. Generate Synthetic Interaction Data
Generating synthetic data is the recommended first step to build a robust, diverse test dataset without manual effort.
2. Compare Prompt Variants
Once your synthetic dataset is ready, use Prompt Flow to iterate on your prompts.
3. Controlled Evaluation
To measure performance objectively, run automated Eval Runs.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/prompt-flow

NEW QUESTION # 105
Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting 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 # 106
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:

You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true.
Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 107
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 108
You have an Azure Machine Learning workspace.
You plan to run a job to tram a model as an MLflow model output.
You need to specify the output mode of the MLflow model.
Which three modes can you specify? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. download
B. ro mount
C. upload
D. direct
E. rw_mount

Answer: B,C,D

NEW QUESTION # 109
......

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