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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Hotspot Question
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
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.
Correct Answer:
Explanation:
Box 1: Model Registry
Deploy resources from a pipeline.
To standardize MLOps practices with declarative infrastructure and automated pipelines, the best choice is to use a Model Registry via Infrastructure as Code (IaC) for your core setup, supplemented by Azure CLI inside your pipelines for operational tasks. Prompt Flow is an application development tool and should not be used to deploy infrastructure.
A Model Registry (like Azure Machine Learning registry or MLflow) is the standard architectural pattern for managing ML artifacts declaratively.
Declarative Tracking: It stores model versions, lineages, and environments as code-like configurations.
Pipeline Integration: CI/CD pipelines can natively fetch from or push to a registry using configuration files.
Environment Isolation: It allows you to promote the exact same model artifact across Dev, Staging, and Production environments without rebuilding.
Box 2: Bicep templates
Define Azure resources declaratively.
Bicep templates are an excellent choice for defining Azure resources declaratively in an automated MLOps pipeline.
Native Azure Integration: Bicep supports all Azure Machine Learning (Azure ML) resources immediately upon release.
Declarative Syntax: You define the desired end-state of your infrastructure without writing complex deployment scripts.
No State Management: Azure manages the state automatically, unlike Terraform which requires a remote state file.
Tooling Support: Deep integration with Azure Pipelines and GitHub Actions allows easy deployment validation.
Reference:
https://azure.microsoft.com/en-us/blog/new-azure-capabilities-to-simplify-deployment-and-management/
Drag and Drop Question
A team operates a generative AI-powered customer support assistant built on Microsoft Foundry.
The application serves users globally and supports both real-time chat interactions and batch summarization jobs.
The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases.
The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity.
You need to select the performance metrics that meet the requirements.
Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric 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.
Correct Answer:
Hotspot Question
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Drag and Drop Question
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
Which three 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.
Correct Answer:
Explanation:
Step 1: Select a metric to measured
You need to choose a baseline performance or fairness metric (such as accuracy rate, precision, or demographic parity) along which the dashboard evaluates the model's overall behavior and cross-group disparities.
Step 2: Select a model feature to evaluated
You must specify a sensitive feature (such as age, gender, or race) to split the data into different subgroups and evaluate potential disparities across them.
Step 3: Select a binary classification or regression model
The Fairlearn package is specifically designed to assess and mitigate unfairness in binary classification and regression tasks. It does not natively support clustering or multiclass models for group fairness assessments in this wizard.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
How should you complete the code?
Box 1: container_name
container_name specifies the parameter name used in the AzureBlobDatastore constructor to identify your target blob storage container.
Box 2: wasbs
Correct Code Formats
Depending on your preference for the storage connection protocol, your completed line of code should look like one of the following variations:
Using the standard HTTPS protocol (Default).
-> Using the WASBS (Windows Azure Storage Blob Secure).
Reference:
https://stackoverflow.com/questions/75275875/create-an-azureblobdatastore-with-sdk-v2
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