Parameter type: stream

Visual features list: VisualFeatureTypes.Description

Result to use: results.Description.Captions[0]

To generate accessible alt text , you should use the caption produced by Azure Computer Vision’s Description feature (it produces a human-readable sentence with a confidence score). Therefore:

The image input should be a stream , because you’re uploading images (not just passing URLs) during product creation and AnalyzeImage…Async supports image streams .

Request the Description feature in VisualFeatureTypes so the service returns results.Description.Captions.

Use results.Description.Captions[0] and return the caption text if its confidence is high enough (e.g., > 0.5) to meet the accessibility requirement that all images must have relevant alt text .

Other features (Tags, Objects, Brands) are useful for enrichment but do not directly return natural-language captions suitable for alt text.

Microsoft Azure AI Solution References

Computer Vision (Image Analysis) – Description/Captions and features: Microsoft Docs, Image Analysis – VisualFeatureTypes.Description returns description.captions.

https://learn.microsoft.com/azure/ai-services/computer-vision/concept-image-analysis

SDK usage (analyze image from a stream): Microsoft Docs, Analyze an image by using the Computer Vision client library .

https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

To present a list of options with images, you use Adaptive Cards, which allow for rich content such as images, buttons, and formatted text in the conversation.

These cards are typically surfaced within the context of a dialog, which controls the flow of the conversation.

Azure functions are unrelated here.

Entities and utterances belong to natural language understanding, not UI presentation.

Correct Answer: B and D

Box 1: Adaptive card

Box 2: an image

Your documents must include a field specifying which groups have access. This information becomes the filter criteria against which documents are selected or rejected from the result set returned to the issuer.

D: A query request targets the documents collection of a single index on a search service.

CF: In order to trim documents based on group_ids access, you should issue a search query with a group_ids/any(g:search.in(g, ' group_id1, group_id2,... ' )) filter, where ' group_id1, group_id2,... ' are the groups to which the search request issuer belongs.

The statement is:

" When using the Azure Cosmos DB Gremlin API, the container resource type is projected as a ______. "

Options:

graph → Correct. The Gremlin API in Azure Cosmos DB is designed to work with graph data models, supporting vertices (nodes) and edges (relationships). A container is projected as a graph.

table → Used when working with the Table API (key/value store). Not correct here.

partition key → Refers to data distribution and scalability, not the container resource type.

document → Refers to the SQL API or MongoDB API in Cosmos DB, not Gremlin.

Therefore, the correct answer is graph.

Correct Answer: graph

Microsoft References

Azure Cosmos DB Gremlin API

Azure Cosmos DB APIs and data models

Normalization involves eliminating relationships between database tables.

No

Normalization does not eliminate relationships. In fact, it often creates relationships between tables by splitting data into smaller, related tables.

Normalizing a database reduces data redundancy.

Yes

The primary goal of normalization is to minimize duplicate data and ensure that each fact is stored only once.

Normalization improves data integrity.

Yes

By reducing redundancy and ensuring data is stored in a structured manner, normalization enforces consistency and integrity across the database.

Correct Responses:

No

Yes

Yes

Microsoft References

Database normalization basics

Database design and normalization

Comprehensive Detailed Explanation

To move a Question Answering project to a different Azure region, you migrate the project data and then re-deploy it in the target region:

Export the existing project (source region).

In Language Studio (Question Answering), you first export the project. This produces a package (JSON/ZIP) containing your knowledge bases, sources, synonyms, and settings. Exporting is the supported way to move a project between resources/regions.

Import the project into the new Language service instance (target region).

In the target region’s Language service instance, use Language Studio to import the previously exported file. This recreates the project (KBs and configuration) under the new resource.

Train and publish from the new instance.

After import, you must train (build) and then publish the project so that an endpoint is created under the new regional resource. Clients can then point to the new endpoint and key.

Actions like “enable custom text classification” or “regenerate the keys” are not required for moving a Question Answering project, and training/publishing from the original instance won’t deploy to the target region.

Microsoft Azure AI Solution References

Export and import Question Answering projects in Language Studio (move between resources/regions):

Microsoft Learn – Export and import projects (Question Answering) .

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/export-import-project

Train and publish a Question Answering project:

Microsoft Learn – Create, train, and publish projects (Question Answering) .

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/create-test-deploy

Language service overview and regional deployment concepts:

Microsoft Learn – What is Azure AI Language?

https://learn.microsoft.com/azure/ai-services/language-service/overview

 The text value must use JSON-formatted text. No

 The command will analyze inputted text and identify whether the text contains published song lyrics. Yes

 The Ocp-Apim-Subscription-Key value must contain the ID of the Azure subscription that hosts Resource1. No

Comprehensive Detailed Explanation

The cURL calls the Protected Material detection endpoint:

POST {endpoint}/contentsafety/text:detectProtectedMaterial?api-version=2024-09-01.

This API expects a JSON body with a text property whose value is a plain string (the content to check). The body is JSON, but the text value itself is not required to be “JSON-formatted text”; it’s just ordinary text inside a JSON payload. Hence, statement 1 is No . Microsoft Learn

The Protected Material (text) feature “flags any known text content … such as song lyrics , articles, recipes, and selected web content.” Therefore, this command will analyze the input and can identify whether it contains published song lyrics. Statement 2 is Yes .

Ocp-Apim-Subscription-Key is the API (resource) key used to authenticate requests to Azure AI services or APIs fronted by API Management. It is not the Azure subscription ID . Authentication guidance explicitly states the header carries a resource key or token; API Management documentation identifies Ocp-Apim-Subscription-Key as the subscription key header. Therefore, statement 3 is No .

Microsoft Azure AI References

Detect Text Protected Material – REST API (endpoint, request/response schema): Microsoft Learn

Protected material detection (concepts; includes song lyrics examples): Microsoft Learn

What’s new in Azure AI Content Safety (mentions protected material detection for lyrics): Microsoft Learn

Azure AI services authentication (resource key in headers): Microsoft Learn

API Management subscriptions (meaning of Ocp-Apim-Subscription-Key): Microsoft Learn

“Monitor online activity” in Content Safety Studio shows usage and moderation results and allows viewing category distributions; it’s meant for observability of live traffic , not for running offline tests on sample questions to tune filter settings. Hence, it doesn’t meet the requirement. Content Safety Studio

References

Azure AI Content Safety – Protected material detection concepts & quickstart.

Azure OpenAI – System message (metaprompt) guidance. Microsoft Learn

Content Safety Studio – Monitor online activity page. Content Safety Studio

Azure OpenAI content filtering system and configuration overview.

The output will include the following words: our and included. → No

The output will include the following words: Paris, Eiffel, and Tower. → Yes

The function will output all the key phrases from the input string to the console. → No

Comprehensive Detailed Explanation

Let’s examine the function:

static void GetKeyWords ( TextAnalyticsClient textAnalyticsClient, string text)

{

var response = textAnalyticsClient.RecognizeEntities(text);

Console.WriteLine( " Key words: " );

foreach (CategorizedEntity entity in response.Value)

{

Console.WriteLine( $ " {entity.Text} " );

}

}

The function uses RecognizeEntities from the Azure Text Analytics SDK.

RecognizeEntities detects and categorizes named entities in text (such as locations, people, organizations, dates, etc.).

It does not extract all words or key phrases.

For extracting key phrases, you would use ExtractKeyPhrases , not RecognizeEntities .

Evaluating the statements:

" The output will include the following words: our and included. "

These are common words, not entities. They will not appear.

Answer: No

" The output will include the following words: Paris, Eiffel, and Tower. "

These are named entities (location, landmark). They will be recognized and returned.

Answer: Yes

" The function will output all the key phrases from the input string to the console. "

No. The function extracts entities, not key phrases. Key phrases would require ExtractKeyPhrases .

Answer: No