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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Security and governance
|
| Topic 2: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 3: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
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NEW QUESTION # 104
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?
Answer: B
Explanation:
A GitHub Actions workflow can absolutely be configured to automatically evaluate pull requests using Azure AI Foundry and fail the CI build if safety thresholds are exceeded.By integrating the Azure AI Evaluation SDK into your automated testing suite, you can scan agentic applications for safety risks (like jailbreaks, hate speech, or content harms) during the continuous integration (CI) lifecycle.
Reference:
https://medium.com/@cataldi.ricardo/compliance-and-governance-app-with-pyrit-39526bd2e4f8
NEW QUESTION # 105
A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?
Answer: B
Explanation:
To restore the service as quickly as possible, you can roll back traffic to the previous deployment by updating the traffic allocation settings of your Azure Machine Learning online endpoint.
Azure Machine Learning managed online endpoints support multiple deployments under a single endpoint, allowing for blue-green deployment strategies where you can shift traffic between versions instantly.
Key Benefits of This Approach
Instant Recovery: Traffic shifting is a routing change and does not require redeploying the previous model's code or environment, making it the fastest recovery method.
No Downtime: Because the previous deployment remains "warm" (active but receiving no traffic), the switch happens without interrupting the service.
Isolation for Debugging: You can keep the failing deployment at 0% traffic to inspect its logs using az ml online-deployment get-logs without affecting end users How to Roll Back Traffic If your new deployment (e.g., "green") is returning errors, you can reallocate 100% of the traffic back to the known stable deployment (e.g., "blue") using the following methods:
* Azure CLI: Use the az ml online-endpoint update command to set the traffic percentage:
az ml online-endpoint update --name <your-endpoint-name> --traffic "blue=100 green=0"
* Azure Machine Learning Studio:
Navigate to Endpoints in the left menu.
Select your specific real-time endpoint.
Go to the Details or Live Traffic tab.
Adjust the traffic percentages so the previous deployment receives 100% and the failing deployment receives 0%.
Select Update or Save to apply the changes immediately
Reference:
https://learn.microsoft.com/en-us/answers/questions/1275110/azure-ml-v2-yaml-code-for-live- traffic-allocation
NEW QUESTION # 106
A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?
Answer: D
Explanation:
A managed online endpoint can host multiple named deployments simultaneously, and you control what percentage of incoming traffic each deployment receives. With traffic splitting, you route a small percentage of live traffic to the new deployment while the majority continues to the proven existing model. You then monitor error rates, latency, and output quality for both deployments in real time under genuine production load. If the new model underperforms, you instantly route traffic back - no downtime, no user disruption. If it outperforms, you gradually increase its traffic share to 100%. Updating the registry version (option B) does not affect running deployments. A staging endpoint (option C) does not validate under real production load.
An evaluation script (option D) is a pre-deployment step. Traffic splitting is the blue/green and canary deployment pattern that Microsoft recommends for safe production rollouts.
Microsoft Learn Reference Topic: Perform safe rollout of new model deployments using traffic splitting - Azure Machine Learning
NEW QUESTION # 107
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance.
You need to maintain the state of the notebook.
What should you do?
Answer: B
NEW QUESTION # 108
You have an Azure subscription named Sub1 that contains an Azure Machine Learning workspace named Workspace1. Workspace1 contains the following assets:
- a registered MLflow model named Model1
- an online endpoint named Endpoint1
Outbound network connectivity from Endpoint1 is blocked.
You need to deploy Model1 to Endpoint1.
What should you do first?
Answer: D
Explanation:
To successfully deploy the registered MLflow model to an online endpoint that lacks outbound internet connectivity, you must use model packaging to build a deployment package before deploying.
By default, Azure Machine Learning deploys MLflow models using a "no-code deployment" approach, which requires an outbound internet connection during container runtime to dynamically download and install Python dependencies listed in the model's conda.yaml file.
Because outbound connectivity is blocked, this process fails. Packaging the model bundles the model binaries, dependencies, and environment ahead of time, entirely removing the requirement for runtime internet access.
Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-deploy-mlflow-models-online-endpoints
NEW QUESTION # 109
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