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| Section | Objectives |
|---|---|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Design and implement generative AI solutions | - Large language model integration
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
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NEW QUESTION # 24
Drag and Drop Question
A team deploys a machine learning model to production and monitors it continuously. Alerts are configured on performance and data quality metrics.
Multiple alerts are triggered during normal operation.
You need to perform the appropriate action for each model alert condition.
Which action should you perform for each alert condition? To answer, move the appropriate actions to the correct model alert conditions. You may use each action 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.
Answer:
Explanation:
NEW QUESTION # 25
You are planning to register a trained model in an Azure Machine Learning workspace.
You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.
You need to register the model.
Which parameter should you use?
Answer: B
Explanation:
azureml.core.Model.properties:
Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model
NEW QUESTION # 26
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named model_train.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: command_component
The @command_component decorator transforms a standard Python function into a reusable pipeline component within Azure ML SDK v2.from model_ Box 2: model_train model_train import train: Because model_train.py resides in the same directory as train_component.py, you import the file directly by its module name (model_train) to access its execution logic inside the component function.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipeline-python
NEW QUESTION # 27
You create an Azure Machine Learning workspace.
You must create a custom role named DataScientist that meets the following requirements:
Role members must not be able to delete the workspace.
Role members must not be able to create, update, or delete compute resource in the workspace.
Role members must not be able to add new users to the workspace.
You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.
The custom role must enforce the restrictions specified by the IT Operations team.
Which JSON code segment should you use?
Answer: C
Explanation:
The following custom role can do everything in the workspace except for the following actions:
It can ' t create or update a compute resource.
It can ' t delete a compute resource.
It can ' t add, delete, or alter role assignments.
It can ' t delete the workspace.
To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named " Data Scientist Custom " scoped at a specific workspace level:
data_scientist_custom_role.json :
{
" Name " : " Data Scientist Custom " ,
" IsCustom " : true,
" Description " : " Can run experiment but can ' t create or delete compute. " ,
" Actions " : [ " * " ],
" NotActions " : [
" Microsoft.MachineLearningServices/workspaces/*/delete " ,
" Microsoft.MachineLearningServices/workspaces/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/delete " ,
" Microsoft.Authorization/*/write "
],
" AssignableScopes " : [
" /subscriptions/ < subscription_id > /resourceGroups/ < resource_group_name > /providers/Microsoft.
MachineLearningServices/workspaces/ < workspace_name > "
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles
NEW QUESTION # 28
An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?
Answer: C
Explanation:
To ensure that older Azure Machine Learning model versions are no longer deployable but remain available for reference or historical tracking, you should archive those specific versions.
How to Archive Model Versions
Archiving a model version effectively hides it from standard list queries and management interfaces, preventing accidental deployment while maintaining its metadata and files in the workspace.
Behavior of Archived Models:
Hidden by Default: They will no longer appear in the Azure Machine Learning Studio model list unless you explicitly filter for archived assets.
Referenceable: You can still reference and use an archived model version in existing workflows if you have its specific version number.
Restoreable: If you need to redeploy the model in the future, you can use the restore command to make it active again.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models
NEW QUESTION # 29
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