Microsoft AI-102 Exam With Confidence Using Practice Dumps

Scenario Recap

You have a web app app1 running on VM1 in vnet1.

You plan to create an Azure Cognitive Search service (service1).

Goal: Ensure app1 can connect to service1 without routing over the public internet.

Proposed Solution

“You deploy service1 and a public endpoint to a new virtual network, and you configure Azure Private Link.”

Analysis

Azure Cognitive Search and Private Link:

Azure Cognitive Search supports Private Endpoints via Azure Private Link.

When you configure a private endpoint, the service is accessible privately over the virtual network and traffic does not go over the public internet.

You can still keep a public endpoint enabled optionally, but access from your VM can be restricted to the private endpoint.

Key Point: The solution specifies using a public endpoint AND a private link.

This still meets the requirement because Private Link ensures that VM1 in vnet1 communicates with service1 privately.

The existence of the public endpoint does not force traffic to go public — traffic from the private endpoint is routed over the VNet.

Conclusion

Yes, this solution meets the goal because Private Link provides secure, private connectivity to Cognitive Search, avoiding the public internet.

Correct Answer: A. Yes

Microsoft References

Secure access to Azure Cognitive Search with Private Link

Azure Private Link overview

To validate that your Azure AI Content Safety resource can identify images containing potential violence, you must call the Image Analyze operation of Content Safety. The REST endpoint for this operation is:

POST {endpoint}/contentsafety/image:analyze?api-version=2024-09-01

This operation analyzes an image for the four harm categories Hate, SelfHarm, Sexual, and Violence. Therefore, the correct base path is /contentsafety, and the correct operation is image analyze (shown in the hotspot as /imageanalyze).

Microsoft References

REST API: Analyze Image — POST {endpoint}/contentsafety/image:analyze?api-version=2024-09-01 ; categories include Hate, SelfHarm, Sexual, and Violence. Microsoft Learn

Quickstart: Analyze image content with Azure AI Content Safety (overview and examples). Microsoft Learn

Harm categories concept (lists Violence among supported categories). Microsoft Learn

Azure AI Content Safety supports custom blocklists —dictionaries of terms/phrases you define to detect obscure or domain-specific offensive terms in user content. Creating a blocklist is the minimal-effort, built-in way to meet this requirement; you don’t need to build a classifier.

References

Create and use blocklists in Azure AI Content Safety.

Text moderation overview (using built-in classifiers with optional blocklists).