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You have an Azure subscription named Sub1 that contains an Azure Machine Learning workspace named Workspace1. Workspace1 contains the following assets:
- a registered MLflow model named Model1
- an online endpoint named Endpoint1
Outbound network connectivity from Endpoint1 is blocked.
You need to deploy Model1 to Endpoint1.
What should you do first?

• A. In Workspace1, create a linked service.
• B. In Sub1, create an Azure Machine Learning registry.
• C. In Sub1, create a private endpoint.
• D. In Workspace1, create a package.

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A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

• A. Assign a resource-level Azure AI Administrator role to the platform engineers.
• B. Assign the Azure AI Developer role to the developers.
• C. Share a single API key across all teams.
• D. Disable Microsoft Entra ID authentication for the Microsoft Foundry resource.

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Drag and Drop Question
A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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.

Drag and Drop Question
You build and manage a model by using Azure Machine Learning workspace.
Before you deploy the model, you must create a Responsible AI dashboard in Azure Machine Learning studio. The dashboard must provide observation of the following:
- metrics that show real-world impact on an outcome of interest due to
taking a treatment policy
- examples with minimal changes to a particular data point such that
the model's prediction changes
You need to implement the components for the Responsible AI dashboard.
Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component 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.

Explanation:
Box 1: Causal analysis
To observe metrics that show the real-world impact on an outcome of interest due to taking a treatment policy, you must implement the Causal analysis component.
Real-World Intervention: Causal analysis specifically uses historical data to evaluate how changing a particular feature (the treatment) directly impacts a real-world outcome.
Policy Making: It generates policy insights by showing which treatment policy is most effective for a specific cohort to optimize your outcome of interest.
Box 2: Counterfactual analysis
You must implement Counterfactual analysis.
The Counterfactual what-if component of the Azure Machine Learning Responsible AI dashboard is designed specifically to "interrogate" a model. It calculates the exact feature tweaks necessary to reverse or modify a specific forecast:
Minimizing Changes: It generates a set of examples demonstrating the smallest possible adjustments to a data point that cause the model's prediction to flip.
Actionable Scenarios: It surfaces the closest data points with opposing or alternative predictions, allowing you to easily test "what-if" scenarios.Why Other Components Do Not Match Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard

Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:

You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.