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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 2: Design and implement generative AI solutions | - Large language model integration
|
| Topic 3: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 79
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible Al (RAI) dashboard for model1.
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 80
A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
Answer: A
Explanation:
To introduce a new model version in Azure Machine Learning without service interruption, you should use Blue/Green Deployment with Traffic Splitting.
This strategy allows you to run two versions of a model simultaneously under a single Online Endpoint, gradually shifting users to the new version once it is validated.
Key Benefits
Zero Downtime: The endpoint URL stays the same; only the backend routing changes.
Easy Rollback: If the new model fails, you can instantly flip traffic back to 100% on the old version.
Risk Mitigation: Only a small subset of users is exposed to the unproven model initially.
Implementation Steps
1. Create the "Green" Deployment
Deploy the new model version as a second deployment under the existing online endpoint.
Initially, set its traffic allocation to 0%.
2. Canary Testing (Initial Split)
Shift a small percentage of traffic (e.g., 10%) to the new deployment. Monitor performance metrics, error rates, and model accuracy in a real-world environment.
3. Validation & Monitoring
Use Azure Monitor and Application Insights to compare the two deployments. Check for:
Latency: Is the new model slower?
HTTP Status Codes: Are there 4xx or 5xx errors?
Model Drift: Is the prediction quality as expected?
4. Full Rollout
If the new version is stable, increase the traffic split (e.g., 50/50) until the new model handles
100% of the traffic.
5. Cleanup
Once the "Green" deployment is confirmed as the new production standard, you can delete the old ("Blue") deployment to save costs.
Reference:
https://learn.microsoft.com/en-us/azure/well-architected/ai/operations
NEW QUESTION # 81
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
Answer: A
NEW QUESTION # 82
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?
Answer: C
NEW QUESTION # 83
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage 1. The storage account includes a publicly accessible container named mlcontainer 1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer 1.
You need to complete the Python SDK v2 code.
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:
Because you want to reference all blobs in a container rather than a single file, the correct asset type is AssetTypes.URI_FOLDER. This type points to a directory-level URI, allowing Azure ML to traverse all files within it. For a publicly accessible Azure Blob Storage container, the URI follows the pattern
https://storage_account.blob.core.windows.net/container_name/. You then create the Data object with the correct type, path, name, and version parameters, and register it in the workspace using ml_client.data.
create_or_update. Do not use AssetTypes.URI_FILE, which references a single file. Do not use AssetTypes.
MLTABLE unless you have an MLTable YAML descriptor. URI_FOLDER is the correct choice for referencing a collection of CSV blobs in an Azure Blob Storage container, allowing Azure ML to discover and process all files within the specified path.
Microsoft Learn Reference Topic: Create and manage data assets in Azure Machine Learning Python SDK v2 - URI_FOLDER
NEW QUESTION # 84
......
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