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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. Manage compute targets, datastores, and environments 2. Configure workspace settings and security
Topic 2: Implement generative AI quality assurance and observability	10–15%	- Evaluate and test generative AI applications 1. Test for safety, accuracy, and relevance 2. Define evaluation metrics and criteria - Monitor generative AI systems 1. Implement logging and alerting 2. Track usage, performance, and errors
Topic 3: 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. Optimize inference and deployment 2. Manage resource utilization
Topic 4: 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. Integrate with Azure services and tools 2. Design scalable and secure architecture
Topic 5: 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. Deploy to real-time and batch endpoints 2. Configure deployment options and scaling - Monitor and maintain models in production 1. Monitor data and model drift 2. Implement retraining and update workflows

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q40-Q45):

NEW QUESTION # 40
you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer.
Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace.
You need to comminute to log training and artifacts from your data science code.
What should you do?

A. Log in to workspace1.
B. Instantiate the MLClient class.
C. Configure the tracking URL.
D. Instantiate the job class.

Answer: C

NEW QUESTION # 41
Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 42
You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:
* Provide near real-time inferencing.
* Enable endpoint and deployment level cost estimates.
* Support logging to Azure Log Analytics.
You need to configure the deployment solution.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 43
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named modeljrain.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
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 # 44
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:

Explanation:
When evaluating a fine-tuned model against the base model using the same evaluation dataset, the interpretation of each metric requires careful analysis. A statement that the fine-tuned model improves on the target task is True if and only if the target metric such as task-specific accuracy, F1 score, or ROUGE score shows a statistically meaningful improvement. A statement about regression on a complementary metric is True if the fine-tuned model ' s score on that metric is meaningfully lower than the base model ' s. In Microsoft Foundry ' s evaluation framework, both pre-fine-tuning and post-fine-tuning results are stored against the same experiment, enabling direct side-by-side comparison. The core principle is that improvement on the primary task is not sufficient if fine-tuning causes degradation on safety or coherence - this is called catastrophic forgetting, and the evaluation dataset is designed to detect it.
Microsoft Learn Reference Topic: Evaluate fine-tuned models in Microsoft Foundry - Compare base and fine- tuned model metrics

NEW QUESTION # 45
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