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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Implement secure and scalable AI systems | - Security and governance
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
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NEW QUESTION # 181
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a datastore in the workspace.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 182
Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
Which four 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
NEW QUESTION # 183
-
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
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:
Correct sequence:
* Deploy a new model version.
* Validate the behavior of the new version.
* Update the model settings on the production route.
The safe approach is to deploy the new model side by side with the existing production deployment rather than modifying the currently serving model immediately. Microsoft Foundry documentation explains that a newly available model version can be tested through a separate deployment, allowing teams to evaluate behavioral differences without affecting existing production requests.
After deployment, validate the behavior of the new version against representative production workloads.
Validation should cover response quality, latency, errors, structured-output compatibility, tool-calling behavior, safety characteristics, and application-specific acceptance criteria. Microsoft ' s model-migration guidance explicitly places validation before production rollout and recommends proving that the new configuration meets defined quality requirements before broad exposure.
Once validation succeeds, update the model settings on the production route so that the application ' s existing production entry point directs requests to the validated deployment. This preserves the stable application-facing endpoint while changing the backend model in a controlled manner.
Shift 100% of traffic to the new version is not the appropriate intermediate safe-promotion action because immediately transferring all production traffic removes the staged validation safeguard. Creating another AI gateway route version is also unnecessary for the required three-step promotion sequence.
Study Guide Reference: Implement machine learning model lifecycle and operations - model versioning, side-by-side deployments, pre-production validation, controlled promotion, production routing, and rollback- safe model lifecycle management.
NEW QUESTION # 184
Drag and Drop Question
A team iterates prompts used by a generative AI agent. The team must support internal review before releasing changes.
The team must:
- Track prompt changes with a clear history for audit and rollback.
- Compare prompt variants in parallel without affecting the prompt used in the production environment.
You need to select the appropriate source control approach for each requirement.
What should you use for each requirement? To answer, move the appropriate source controls to the correct requirements. You may use each source control 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 # 185
you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer.
Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace.
You need to comminute to log training and artifacts from your data science code.
What should you do?
Answer: C
NEW QUESTION # 186
......
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