AI-102 Exam Dumps : Designing and Implementing a Microsoft Azure AI Solution

Can search content in different formats, including video

Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.

Keywords extraction: Extracts keywords from speech and visual text.

Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).

Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.

Artifacts: Extracts rich set of " next level of details " artifacts for each of the models.

Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.

Use " definition " for the first field and " sampleBlobUrl " for the second field

When creating a custom text category in Azure AI Content Safety, you call the PUT /contentsafety/text/categories/{categoryName} endpoint and provide a JSON body that includes:

categoryName : the identifier of your category.

definition : a natural-language description of what the category covers (e.g., “text prompts about learning advice while preparing for an exam”).

sampleBlobUrl : a pointer to labeled sample data (typically a JSONL file in Azure Storage) used to help the service understand the category.

Keys like prompt or text are used when analyzing content (for example, text:detect ), not when defining a category. Likewise, blocklistName applies to blocklist operations, and outputType is not part of the category-creation schema. Therefore, the correct payload fields to complete the cURL are definition and sampleBlobUrl .

Microsoft Azure AI References (titles only)

Azure AI Content Safety – Create or update custom text categories (REST)

Azure AI Content Safety – Custom categories concepts and training data (JSONL in blob storage)

Azure AI Content Safety – Text analysis vs. category management endpoints

The question is about creating an Azure Cognitive Services resource programmatically with C#.

Key requirements from the question:

Free resource → This requires the F0 (Free) pricing tier.

West US region → Location should be " westus " .

Generate captions of images automatically → This requires the Computer Vision service.

Option Analysis

A. create_resource(client, " res1 " , " ComputerVision " , " F0 " , " westus " )

Uses " ComputerVision " kind → Correct, because Computer Vision provides the image captioning feature.

Tier is " F0 " → Correct, free tier.

Location " westus " → Correct.

This is the correct answer.

B. create_resource(client, " res1 " , " CustomVision.Prediction " , " F0 " , " westus " )

Custom Vision is for building and deploying custom image classifiers.

The Prediction resource is specifically for running inference, not general captioning.

Wrong service for the requirement.

C. create_resource(client, " res1 " , " ComputerVision " , " S0 " , " westus " )

" ComputerVision " is correct.

But " S0 " is a paid tier, not the required free tier.

Does not meet the requirement.

D. create_resource(client, " res1 " , " CustomVision.Prediction " , " S0 " , " westus " )

Wrong service (captioning is not handled by Custom Vision).

Wrong tier (paid instead of free).

Incorrect.

Correct Answer: A. create_resource(client, " res1 " , " ComputerVision " , " F0 " , " westus " )

Microsoft References

Azure AI Computer Vision documentation

Create a Cognitive Services resource in Azure

Computer Vision capabilities – Image analysis and captioning