Microsoft AI-102 Exam With Confidence Using Practice Dumps

In the Azure AI Translator .NET SDK (Azure.AI.Translation.Text), the TextTranslationClient exposes TranslateAsync overloads that accept a target language (e.g., "fr") and an input string to translate. The call returns a Response>. Each TranslatedTextItem includes the automatically detected source language (DetectedLanguage.Language) and a collection of translations in Translations. To print the translated text for the first target language, read translation.Translations[0].Text.

Therefore:

Use TranslateAsync(targetLanguage, input) to perform the translation.

Output translation.Translations[0].Text to display the translated result.

Microsoft References

TextTranslationClient.TranslateAsync method (overloads, usage). Microsoft Learn

TextTranslationClient class overview. Microsoft Learn

TranslatedTextItem model and Translations property (array of translation results).

Quickstart: Translator text client libraries (end-to-end usage pattern). Microsoft Learn

The app must:

Identify road signs and extract text from images

This is an OCR (Optical Character Recognition) task.

Azure AI Vision provides OCR capabilities to detect and extract text from images (including road signs).

Correct choice: Azure AI Vision.

Analyze the text to identify well-known locations

Once text is extracted, you need to detect if it refers to well-known places (e.g., cities, landmarks).

This is a Named Entity Recognition (NER) problem.

Azure AI Language service includes entity recognition for identifying locations, people, organizations, etc.

Correct choice: Azure AI Language.

Other options:

Azure AI Document Intelligence → Focused on structured documents (invoices, forms), not road signs.

Azure AI Search → Useful for indexing and search, not text extraction or entity recognition.

Correct Answer Matrix:

Extract the text → Azure AI Vision

Identify well-known locations → Azure AI Language

Azure AI Vision – OCR text extraction

Azure AI Language – Named Entity Recognition (NER)

Microsoft References

Box 1: "categories": ["Locations", "Persons", "Organizations"],

Locations, Persons, Organizations are in the outputs.

Scenario: Contoso plans to develop a searchable knowledgebase of all the intellectual property

Note: The categories parameter is an array of categories that should be extracted. Possible category types: "Person", "Location", "Organization", "Quantity", "Datetime", "URL", "Email". If no category is provided, all types are returned.

Box 2: {"name": " entities"}

The include wikis, so should include entities in the outputs.

Note: entities is an array of complex types that contains rich information about the entities extracted from text, with the following fields

name (the actual entity name. This represents a "normalized" form)

wikipediaId

wikipediaLanguage

wikipediaUrl (a link to Wikipedia page for the entity)

etc.