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

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.

Fabrikam Inc. must improve its deployment process because traditional machine learning models are deployed manually and the organization has limited rollback capability .
You need to recommend a deployment approach that supports staged rollout and rollback while minimizing operational overhead.
Which deployment approach should you recommend?

• A. Batch endpoints
• B. VM-hosted REST APIs
• C. Azure Kubernetes Service with blue-green switching
• D. Managed online endpoints with traffic splitting

A data science team trains a model that depends on features that are stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
* Retrieve feature values dynamically at inference time.
* Use the same feature definitions that were used during training.
* Run without manual configuration changes across environments.
You need to define feature store entities so that feature retrieval behaves as expected when the model is deployed.
Which feature store entity should you select for each requirement? To answer, move the appropriate feature store entities to the correct requirements. You may use each feature store entity 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.

You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
NOTE: Each correct selection is worth one point.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?

• A. No
• B. Yes