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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - Large language model integration
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Implement secure and scalable AI systems | - Security and governance
|
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質問 # 102
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.
Model performance has degraded due to model input data changes.
You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.
Which event should you use?
正解：D
質問 # 103
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.
正解：A、B
解説：
[A]
Engineer Permissions (Hub Scope)
Engineers require the ability to manage infrastructure, networking, and global security settings.
Role: Azure AI Administrator or Contributor.
Assignment Scope: Assign at the Foundry Hub/Resource level.
Capabilities: They can manage virtual networks, customer-managed keys, and shared connections (e.g., Azure OpenAI) that all projects inherit.
[C]
Developer Permissions (Project Scope)
Developers should be restricted from changing the underlying resource configurations but need full access to their specific AI workloads.
Role: Azure AI Developer or Project Contributor.
Assignment Scope: Assign strictly at the Foundry Project level.
Capabilities: This allows them to create and update project assets (agents, flows, evaluations) and deploy models without permission to modify Hub-level infrastructure or security settings Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/rbac-foundry
質問 # 104
Drag and Drop Question
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
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.
正解：
解説：
質問 # 105
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 a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?
正解：A
解説：
For Azure Machine Learning SDK v2, Microsoft recommends MLflow Tracking for experiment logging.
When a requirement specifically calls for logging a list of numerical metric values , the appropriate operation is log_batch() . Microsoft provides an explicit SDK v2 migration example in which a list of numeric values is converted into MLflow Metric objects and submitted through MlflowClient().log_batch().
Technically, the concrete Python implementation uses the tracking client:
MlflowClient().log_batch(run_id, metrics=metrics)
where metrics contains a sequence of Metric objects representing the numerical values. Microsoft likewise documents batch metric logging as preferable when multiple metrics or multiple values of the same metric need to be recorded efficiently.
mlflow.log_metric() records an individual numeric metric value. It can be invoked repeatedly, but Microsoft specifically demonstrates log_batch() for a curve or list of numeric values . mlflow.log_artifact() stores files and other artifacts rather than numerical tracking metrics, while mlflow.log_image() is intended for images and therefore does not satisfy the requirement.
Therefore, A is the intended answer. In actual Python SDK v2 code, this operation is normally invoked as MlflowClient().log_batch() .
Study Guide Reference: Implement machine learning model lifecycle and operations - Azure Machine Learning experiments, MLflow tracking, metric logging, experiment observability, and SDK v2 migration.
質問 # 106
You create an Azure Machine Learning workspace.
You must configure an event-driven workflow to automatically trigger upon completion of training runs in the workspace. The solution must minimize the administrative effort to configure the trigger.
You need to configure an Azure service to automatically trigger the workflow.
Which Azure service should you use?
正解：D
質問 # 107
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