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| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 3: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 4: Implement secure and scalable AI systems | - Scalability and performance optimization
|
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NEW QUESTION # 33
Hotspot Question
You create an Azure Machine Learning workspace.
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script, environment, and compute information.
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: MLClient
Box 2: command
Example, Submit a script run, SDK v2
#connect to the workspace
ml_client = MLClient.from_config(DefaultAzureCredential())
# set up pytorch environment
env = Environment(
image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04",
conda_file="pytorch-env.yml",
name="pytorch-env"
)
# define the command
command_job = command(
code="./src",
command="train.py",
environment=env,
compute="cpu-cluster",
)
returned_job = ml_client.jobs.create_or_update(command_job)
returned_job
Note: create_or_update
Creates or updates an Azure ML resource.
create_or_update(entity: T, **kwargs) -> T
Parameters
entity
Union[Job , Model, Environment, Component , Datastore]
Required
The resource to create or update.
Returns
The created or updated resource.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job
https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.mlclien
NEW QUESTION # 34
Drag and Drop Question
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities.
You may use each tool 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 # 35
An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?
Answer: B
Explanation:
To ensure an Azure Machine Learning (AML) workspace handling sensitive data is not accessible from the public internet, you must disable the Public Network Access flag and implement Private Endpoints. This configuration creates a private link between your Azure Virtual Network (VNet) and the workspace, ensuring traffic never traverses the public internet.
Reference:
https://www.azadvertizer.net/azpolicyadvertizer/438c38d2-3772-465a-a9cc-7a6666a275ce.html
NEW QUESTION # 36
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.
Answer:
Explanation:
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
NEW QUESTION # 37
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.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
This action is best because it fulfills all of your operational requirements:
Consistency & Controlled Manner:
Infrastructure as Code (IaC) via Bicep ensures that dev, test, and prod environments are configured identically, eliminating configuration drift.
Source Control: Storing the Bicep template in a Git repository satisfies the requirement that each environment must be updated through source control.
Automated & Repeatable: Orchestrating the deployment using GitHub Actions fully automates the workflow, allowing it to be executed reliably every time a change is merged.
Auditable: Git commit histories combined with GitHub deployment logs provide a comprehensive, compliant audit trail of exactly who modified the infrastructure and when.
Incorrect:
* Clone an existing Azure Machine Learning workspace to create additional environments.
* Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
NEW QUESTION # 38
......
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