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AI-103 Practice Question 92: Azure Policy Model Governance

Microsoft Azure AI Apps and Agents Developer Associate (AI-103) · Test 10 · question 2 of 10

Plan and manage an Azure AI solutionmedium

A platform team is standardizing Microsoft Foundry usage across dozens of subscriptions owned by different business units. Leadership's rule: only models published directly by Microsoft may be deployed, no model still in preview status may reach production, and the rule must apply enterprise-wide without the team having to maintain and update an explicit list of every model version that is or isn't allowed. Deployments that violate the rule must be blocked outright, not just logged for later review.

What should the platform team do?

Select one answer
Show answer and explanation

Answer:

  • A. Assign the built-in "Foundry model deployments should meet eligibility requirements" policy at the management group scope, with onlyAllowDirectFromAzure and denyPreviewModels set to true and the effect set to Deny

The eligibility requirements policy answers "does this model meet my organization's standards for source and maturity?" using attributes rather than identity; its two parameters are exactly onlyAllowDirectFromAzure and denyPreviewModels, and Microsoft's own framing is that it's useful "when you want to enforce broader organizational standards... without maintaining an explicit allow list." Best practice for scope is to assign "at the appropriate scope... management group, subscription, or resource group"; a management group assignment covers every subscription beneath it in one policy assignment, and setting the effect to Deny blocks noncompliant deployments outright rather than only auditing them.

- B is wrong on two counts: the approved models policy requires exactly the explicit allow-list the team wants to avoid maintaining, and assigning it "in each subscription" doesn't scale the way a single management-group assignment does. - C is wrong because an RBAC role assignment controls *who* can create a deployment, not *which* models are eligible; it doesn't encode either of the two stated rules about source or preview status. - D is wrong because Defender for Cloud recommendations are detective controls that surface noncompliant resources after the fact; the requirement is to block the deployment outright, which only a Deny-effect policy assignment does.

Reference: Built-in policies for model deployment

Practice Test 10 ›

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