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| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
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NEW QUESTION # 41
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: D
Explanation:
According to Microsoft ' s Azure Machine Learning documentation on managed online endpoints, a single endpoint can host multiple named deployments simultaneously, and you can control what percentage of incoming traffic each deployment receives. This traffic-splitting capability is the foundation of blue/green and canary deployment strategies. When you route 10% of traffic to a new deployment and 90% to the existing one, you can monitor error rates, latency, and output quality of the new version under real production load without risking the majority of users. Microsoft explicitly recommends this pattern for safe rollouts. Option A (batch endpoint) is inappropriate for real-time serving. Option C (replacing the endpoint) is high-risk with no validation window. Option D (routing all traffic to the new deployment) provides zero protection if the new version has issues.
Microsoft Learn Reference Topic: Safe rollout of machine learning models - Traffic splitting on managed online endpoints
NEW QUESTION # 42
Drag and Drop Question
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
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:
Step 1: Select a metric to measured
You need to choose a baseline performance or fairness metric (such as accuracy rate, precision, or demographic parity) along which the dashboard evaluates the model's overall behavior and cross-group disparities.
Step 2: Select a model feature to evaluated
You must specify a sensitive feature (such as age, gender, or race) to split the data into different subgroups and evaluate potential disparities across them.
Step 3: Select a binary classification or regression model
The Fairlearn package is specifically designed to assess and mitigate unfairness in binary classification and regression tasks. It does not natively support clustering or multiclass models for group fairness assessments in this wizard.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
NEW QUESTION # 43
Drag and Drop Question
An organization is adopting Microsoft Foundry to support multiple generative AI projects across different teams. Microsoft Foundry platform administrators require centralized governance.
Development teams need isolated environments for experimentation and deployment.
Shared policies must be enforced consistently while allowing teams to work independently.
You need to configure the Microsoft Foundry environment to meet the requirements.
Which Microsoft Foundry components should you configure? To answer, move the appropriate configurations to the correct responsibilities. You may use each configuration 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 # 44
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true.
Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 45
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting 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:
Explanation:
For deployment failures when containers do not start, review deployment logs in Azure ML Studio or use the CLI get-logs command to surface dependency installation errors, missing files, or scoring script import failures. For authentication errors such as 401 or 403 responses, check that client applications are using valid authentication keys or bearer tokens, as mismatched or expired credentials cause auth failures. For performance degradation and slow responses, review compute instance scaling configuration, since a surge in traffic saturates a fixed number of replicas causing queuing and high latency without autoscaling. For internal server errors such as HTTP 500, these typically originate in the scoring script, so inspect the init and run functions for unhandled exceptions and verify the environment has all required packages installed.
Microsoft Learn Reference Topic: Troubleshoot online endpoint deployment failures - Azure Machine Learning
NEW QUESTION # 46
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