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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 machine learning model lifecycle and operations	25–30%	- Orchestrate model training and experimentation 1. Create and manage pipelines 2. Track experiments and metrics - Register, version, and package models 1. Create reusable model packages 2. Manage model registry - Monitor and maintain models in production 1. Implement retraining and update workflows 2. Monitor data and model drift - Deploy models to production 1. Deploy to real-time and batch endpoints 2. Configure deployment options and scaling
Topic 3: 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
Topic 4: 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. Manage resource utilization 2. Optimize inference and deployment
Topic 5: 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

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1
You manage an Azure Machine Learning workspace That has an Azure Machine Learning datastore.
Data must be loaded from the following sources:
* a credential-less Azure Blob Storage
* an Azure Data Lake Storage (ADLS) Gen 2 which is not a credential-less datastore You need to define the authentication mechanisms to access data in the Azure Machine Learning datastore.
Which data access mechanism should you use? To answer, move the appropriate data access mechanisms to the correct storage types. You may use each data access mechanism 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.

Question #2
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?

A. Use the create_or_update method to change the tag of the image.
B. Modify the conda_file to specify the new version of the Docker image.
C. Change the description parameter of the environment configuration.
D. Use the Environment class to create a new version of the environment.

Question #3
You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.
You start by writing the following code segment:

For each of the following statements, select Yes If the statement is true. Otherwise, select No.

Question #4
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question #5
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. JSON
B. TSV
C. CSV
D. JSONL

Solutions:

Question #1
Correct Answer: Only visible for members

Question #2
Correct Answer: D

Question #3
Correct Answer: Only visible for members

Question #4
Correct Answer: Only visible for members

Question #5
Correct Answer: D