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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q49-Q54):

NEW QUESTION # 49
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?

Answer: D

Explanation:
An appropriate base model for fine-tuning with text and image inputs in the Microsoft Foundry portal is GPT-4o (specifically versions like gpt-4o-2024-05-13 or newer). This model supports multi-modal training data, allowing you to improve performance on tasks requiring both visual understanding and textual response within Azure OpenAI Service.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-fine-tuning- capabilities-with-images-on-azure-openai-service/4303695


NEW QUESTION # 50
An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?

Answer: C

Explanation:
To ensure that older Azure Machine Learning model versions are no longer deployable but remain available for reference or historical tracking, you should archive those specific versions.
How to Archive Model Versions
Archiving a model version effectively hides it from standard list queries and management interfaces, preventing accidental deployment while maintaining its metadata and files in the workspace.
Behavior of Archived Models:
Hidden by Default: They will no longer appear in the Azure Machine Learning Studio model list unless you explicitly filter for archived assets.
Referenceable: You can still reference and use an archived model version in existing workflows if you have its specific version number.
Restoreable: If you need to redeploy the model in the future, you can use the restore command to make it active again.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models


NEW QUESTION # 51
You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?

Answer: C

Explanation:
When labeled data is unavailable, traditional accuracy metrics cannot be computed. Data drift detection monitors changes in input data distribution, serving as a proxy for potential performance degradation. This allows early detection of issues before labeled data becomes available.


NEW QUESTION # 52
An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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:
The two requirements target fundamentally different stages of the AI application lifecycle. Continuous runtime monitoring addresses production behavior - an application that intermittently times out needs real- time visibility into latency, error rates, and request volumes for every call in production. Tracing backed by Application Insights or OpenTelemetry exporters is correct here because it captures granular, per-request telemetry continuously without requiring manual triggering - it is always-on by design. Release-time validation addresses pre-release quality gates - before new prompts or model updates go live, you need to verify they have not introduced regressions. An evaluation pipeline in Microsoft Foundry ' s prompt flow is correct here: it runs the updated application against a predefined test dataset, scores outputs on quality and safety metrics, and passes or fails the release based on thresholds. Evaluation pipelines run on demand triggered by CI/CD, not continuously.
Microsoft Learn Reference Topic: Microsoft Foundry observability - Tracing for runtime monitoring vs.
Evaluation pipelines for release gates


NEW QUESTION # 53
A company ' s platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .

Answer: A,B

Explanation:
Microsoft ' s Azure AI Foundry documentation defines built-in roles scoped to the resource hierarchy. The Azure AI Administrator role grants permissions to manage the Azure AI Hub resource itself - including network settings, shared connections, quota, and governance configuration - which is appropriate for platform engineers who configure resource-level settings. The Azure AI Developer role grants permissions to create, update, and manage project-level assets such as deployments, prompt flows, fine-tuning jobs, and evaluations, without access to resource-level configuration. This precisely matches the developer requirement and enforces least privilege. Disabling Entra ID authentication (option B) violates security policy and removes the identity-based access control that makes RBAC possible. Sharing a single API key (option D) violates the least-privilege principle - all users would have identical, undifferentiated access with no audit trail.
Microsoft Learn Reference Topic: Manage access to Azure AI Foundry - Built-in roles: Azure AI Administrator and Azure AI Developer


NEW QUESTION # 54
......

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