Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) - AI-102日本語 Exam Practice Test

Explanation:

1. api-nam.cognitive.microsofttranslator.com
2. translate
https://learn.microsoft.com/en-us/azure/cognitive-services/Translator/reference/v3-0-reference#base-urls Requests to Translator are, in most cases, handled by the datacenter that is closest to where the request originated. If there ' s a datacenter failure when using the global endpoint, the request may be routed outside of the geography.
To force the request to be handled within a specific geography, use the desired geographical endpoint. All requests are processed among the datacenters within the geography.
- United States
api-nam.cognitive.microsofttranslator.com
https://learn.microsoft.com/en-us/azure/cognitive-services/translator/reference/rest-api-guide
- translate
Translate specified source language text into the target language text.

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:
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:

Explanation:

To quickly add new product classes to an existing image classifier with minimal development effort, use the Custom Vision portal workflow. Vision Studio and Azure Machine Learning studio are not required for Custom Vision projects. The standard lifecycle to update a classifier is: open the project, add/upload images, label them with the correct tags, retrain to incorporate the new labels, and publish the updated iteration so the app can consume the new model via the Prediction endpoint.
Microsoft Azure AI Solution References
Azure AI Services - Custom Vision : add images, tag, train, and publish iterations from the portal.
Custom Vision documentation - Quickstart: Build a classifier in the Custom Vision website (steps: create
/open project, upload images, tag, train, publish).

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Explanation:
semi-structured data
The statement is:
" Varying fields for each entity in a JSON document is an example of ______. " Relational data # Stored in structured tables with fixed schema (e.g., SQL database). Not applicable here.
Structured data # Has a strict schema (columns and types are predefined). JSON allows variation, so not structured.
Semi-structured data # Data that does not require a fixed schema, but still uses markers (tags, keys) to separate elements. JSON and XML are classic examples. # Unstructured data # Data without schema or labeling (e.g., videos, images, free text). Not JSON.
Since JSON documents can have varying fields per entity (flexible schema), this is considered semi-structured data.
The answer: semi-structured data
Microsoft References
Structured, semi-structured, and unstructured data
JSON in Azure

Explanation:

Box 1: Yes
You create property accessors using the CreateProperty method that provides a handle to the BotState object.
Each state property accessor allows you to get or set the value of the associated state property.
Box 2: Yes
Box 3: No
Before you exit the turn handler, you use the state management objects ' SaveChangesAsync() method to write all state changes back to storage.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-v4-state

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