Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) - AI-102 Korean Exam Practice Test

Explanation:
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

Explanation:

Comprehensive Detailed Explanation
In an Azure Cognitive Search knowledge store, you can project enriched content into three main forms:
File projection # For storing binary or unstructured documents (e.g., scanned PDFs, images).
Object projection # For storing JSON objects (hierarchical/unstructured JSON data).
Table projection # For storing structured, tabular results (relational data).
Case 1: JSON data
JSON is already structured/unstructured textual data.
The right projection type for JSON in the knowledge store is Object projection.
Case 2: Scanned data
Scanned PDF/image data is binary file-based content.
The right projection type is File projection, so you can store the original scanned file along with extracted text.
Final Answer (Answer Area)
JSON data: Object projection
Scanned data: File projection
Microsoft References
Azure Cognitive Search knowledge store projections
Object vs File vs Table projections

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Explanation:
Box 1: findsimilars
https://docs.microsoft.com/en-us/rest/api/faceapi/face/find-similar
Box 2: matchPerson
Find similar has two working modes, " matchPerson " and " matchFace " . " matchPerson " is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person ' s other photos. Note that an empty list will be returned if no faces pass the internal thresholds. " matchFace " mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/detectwithurl
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar

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Explanation:

This scenario is about improving the accuracy of QnA Maker responses when user phrasing is different but semantically the same.
Problem
Uploaded document contains: "What is your warranty period?"
Users ask: "How long is the warranty coverage?"
QnA Maker fails because it does not automatically equate these two phrasings.
Both must map to the same answer.
Correct Sequence of Actions
Add alternative phrasing to the question and answer (QnA) pair.
In QnA Maker, you can add synonyms or paraphrases for a question.
Example: Add "How long is the warranty coverage?" as an alternative phrasing to the existing QnA pair.
This ensures the system treats both phrasings as the same question.
Retrain the model.
After modifying the knowledge base with new alternatives, you must retrain the model.
This step incorporates the updates into the QnA Maker service.
Republish the model.
To make the changes live and accessible by the chatbot endpoint, the retrained model must be republished.
Without republishing, the chatbot will still use the old knowledge base.
Actions That Are Not Required
Add a new QnA pair # Not needed, because we want both questions to map to the same existing answer, not duplicate answers.
Add additional questions to the document # That's not required when QnA Maker already supports adding alternative phrasing within the portal or JSON editor.
Final Answer (Sequence)
Add alternative phrasing to the question and answer (QnA) pair.
Retrain the model.
Republish the model.
Microsoft References
QnA Maker - Add alternative phrasing
Train and publish a QnA Maker knowledge base

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Explanation:
The POST request starts an asynchronous analysis job for video01.mp4. Yes You use the URL in the Operation-Location header to poll and retrieve the results . Yes The POST does not return the full transcript/key frames in the response body (only 202 Accepted). No When you invoke a custom analyzer in Azure AI Content Understanding using the analyzers/{analyzerId} endpoint with API version 2024-12-01-preview , the service runs as a long-running operation. The initial response is 202 Accepted and includes an Operation-Location header pointing to the results URL. You then GET that URL to check status and, once complete, obtain outputs (for a video analyzer, this includes transcription and key frames because those capabilities are part of the analyzer definition). The header Ocp- Apim-Subscription-Key is valid for this API.
Microsoft References
Quickstart (REST): asynchronous analyze call returns 202 with Operation-Location ; poll to get results.
Microsoft Learn
What's new: API version 2024-12-01-preview . Microsoft Learn
Prebuilt video analyzer functions (transcript + key frames). Custom analyzers inherit configured tasks.
Microsoft Learn
REST API surface: Get Result and related operations for analyzers. Microsoft Learn Security header definition ( Ocp-Apim-Subscription-Key ). Microsoft Learn

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Explanation:

Box 1: QnA Maker Editor
Scenario: Provide all employees with the ability to edit Q & As.
The QnA Maker Editor (read/write) has the following permissions:
Create KB API
Update KB API
Replace KB API
Replace Alterations
" Train API " [in
new service model v5]
Box 2: Contributor
Scenario: Only senior managers must be able to publish updates.
Contributor permission: All except ability to add new members to roles
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/reference-role-based-access-control

Explanation:
Correct Sequence:
Add the prebuilt domain ToDo
Add a new application
Train the application
Publish the application
Comprehensive Detailed Explanation
You are building a Conversational Language Understanding (CLU) model for a chatbot that supports task tracking. To minimize development time, the best approach is to use a prebuilt domain (like ToDo) instead of building intents/entities manually.
Steps in correct order:
Add the prebuilt domain ToDo
Prebuilt domains in CLU provide ready-to-use intents and entities for common scenarios (e.g., calendar, email, ToDo).
This significantly reduces development time since you don't need to create all intents/entities manually.
Add a new application
You must first create a CLU application that uses the prebuilt domain.
The application acts as the container for intents, entities, and utterances.
Train the application
Training is required after adding intents, entities, and utterances (or using a prebuilt domain).
This ensures the model can classify and extract information correctly.
Publish the application
Once trained, you must publish the application so it can be accessed via an endpoint and integrated into the chatbot.
Incorrect Actions in this Case
Add example utterances # Not required if you're using the prebuilt ToDo domain (utterances are already included).
Correct Sequence:
Add the prebuilt domain ToDo
Add a new application
Train the application
Publish the application
Microsoft References
Conversational Language Understanding (CLU) overview
Prebuilt domains in CLU (LUIS ToDo example)
Train and publish CLU models