Microsoft Operationalizing Machine Learning and Generative AI Solutions : AI-300 Exam Questions

Exam Code: AI-300
Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Updated: Oct 11, 2026
Q&As: 189 Questions and Answers

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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
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
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
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. Track usage, performance, and errors 2. Implement logging and alerting
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
Implement machine learning model lifecycle and operations	25–30%	- 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. Implement retraining and update workflows 2. Monitor data and model drift - Register, version, and package models 1. Manage model registry 2. Create reusable model packages

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1

You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics. You need to implement the method to log the string metrics. Which method should you use?

A. mlflow.log_artifact()
B. mlflow.log text()
C. mlflowlog_metrk()
D. mlflow.log.dict()

Reveal Solution Discussion 0

Correct Answer: B 🗳️

Question #2

A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. 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.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Azure Machine Learning infrastructure can be described as Infrastructure as Code using ARM templates, Bicep, or Terraform. To make provisioning fully automated, two separate concerns must be addressed. First, the trigger: a CI/CD system like GitHub Actions or Azure DevOps Pipelines must detect changes to IaC template files in the repository, configured through a branch-push or pull-request trigger. Second, the execution: the CI/CD pipeline runs the provisioning commands such as az ml workspace create or bicep deploy non-interactively using a service principal or managed identity, eliminating the need for a human to run scripts manually. Together, these two mechanisms deliver the GitOps-style automation that modern MLOps practices require, ensuring every template change automatically results in a consistent, auditable infrastructure update.
Microsoft Learn Reference Topic: Automate Azure Machine Learning with GitHub Actions - CI/CD for ML infrastructure

Question #3

-
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Load the model when the service starts: init()
Use the model to score new data: run()
Azure Machine Learning scoring scripts for online endpoints use two required entry-point functions: init() and run() . Microsoft explicitly states that the scoring script specified for an online deployment must contain both functions.
The init() function is invoked when the inference container is initialized or started, typically immediately after a deployment is created or updated. It is intended for one-time initialization tasks such as locating the registered model through AZUREML_MODEL_DIR, deserializing the model, and storing it in memory.
Loading the model once during initialization avoids repeatedly loading it for every inference request, which reduces latency and processing overhead.
The run() function is called each time the endpoint receives an inference request. It accepts the incoming request data, transforms or parses the input as required, invokes the loaded model ' s prediction logic, and returns the scoring result. Microsoft describes run() as the function that performs the actual scoring or prediction for each endpoint invocation.
main(), score(), and predict() may exist inside application code or model libraries, but they are not the required Azure Machine Learning scoring-script entry points.

Question #4

You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.wmdows.net/data1.
You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time.
You need to select the parameters of the Azure Blob Datastore class that will point to the target datastore and authorize access to it.
Which parameters should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:

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 for multi-turn chat.
Which file encoding method should you use?