A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?

• A.Deploy the model to a batch endpoint.
• B.Split traffic between deployments.
• C.Replace the existing endpoint.
• D.Route all traffic to the new deployment.

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An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least- privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration 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.

Correct Answer:

Explanation:
Managed Identity is Azure ' s solution for eliminating stored secrets and API keys from application configurations. When a Microsoft Foundry resource or its dependent compute has a system-assigned or user- assigned managed identity, it can authenticate to Azure services such as Storage, Key Vault, and Cognitive Services using Azure Entra ID tokens that are automatically issued and rotated by Azure. There are no keys to store, rotate, or accidentally expose in code or configuration files, directly satisfying the requirement to authenticate without stored credentials. Role-Based Access Control (RBAC) is the Azure governance mechanism for scoping permissions. By assigning specific built-in roles to the managed identity or to user and group principals at the correct scope - subscription, resource group, resource, or project level - the organization can enforce least-privilege access across different projects and shared resources.
Microsoft Learn Reference Topic: Configure managed identities and RBAC for Microsoft Azure AI Foundry resources

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A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.
The team working on the model must ensure the following:
Changes in input data distribution are detected.
Appropriate actions are triggered when predefined thresholds are exceeded.
You need to configure monitoring to meet the requirements.
Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area . NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Azure Machine Learning ' s data drift monitor solves the first requirement: it continuously compares the statistical distribution of live inference input features against the baseline training data distribution, flagging when significant drift occurs. For the second requirement of triggering appropriate actions when thresholds are exceeded, Azure Monitor alert rules are configured on the drift metrics. When the drift coefficient exceeds a defined threshold, Azure Monitor fires an alert that can invoke Logic Apps, send emails, trigger an Azure ML retraining pipeline, or post to Teams. This two-layer approach - ML-specific drift detection backed by Azure Monitor alerting - is the Microsoft-recommended pattern for production model monitoring. The data drift monitor handles detection, while Azure Monitor handles the operational response, keeping the two concerns cleanly separated.
Microsoft Learn Reference Topic: Monitor model data drift - Azure Machine Learning model monitoring and Azure Monitor integration

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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.

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You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?

• A.Same compute SKU
• B.Same dataset name
• C.Versioned datasets and environments
• D.Same user identity

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