Explanation:

The chatbot needs to recognize two different kinds of information:
* Code names for internal product development
* These are not standard entities like "locations" or "organizations" that prebuilt NER can detect.
* You need to train a custom model to recognize internal terms or codenames (for example,
"Project Falcon").
* The correct Azure Cognitive Service for Language feature is Custom Named Entity Recognition (Custom NER), which allows defining and training entity categories specific to your business.
* Messages that include credit card numbers
* Credit card numbers are sensitive data falling under Personally Identifiable Information (PII).
* Azure Cognitive Service for Language provides a PII detection feature that automatically identifies and masks sensitive information such as credit card numbers, SSNs, and phone numbers.
* This minimizes development effort since it is prebuilt and ready to use.
Correct Answer Mapping:
* Identify code names for internal product development # Custom named entity recognition (NER)
* Identify messages that include credit card numbers # Personally Identifiable Information (PII) detection
* Custom Named Entity Recognition in Azure AI Language
* PII detection in Azure AI Language

Explanation:


The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.
* Product Name Display
* The product JSON stores localized names like "name": { "en": "...", "es": "...", "pt": "..." }.
* To dynamically render based on the current language, you must use name[language].
* This ensures the product name shown in the card adapts automatically to the user's preferred language.
* Stock Level Warning
* Business rules require warnings when stock is low or out of stock.
* This means you display the stock warning only when stockLevel is not 'OK'.
* Therefore, the correct condition is:"$when": "${stockLevel != 'OK'}"
* Image Alt Text for Accessibility
* Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.
* In the product JSON, "alttext" is also localized:
* "altText": { "en": "Bicycle", "es": "Bicicleta", "pt": "Bicicleta" }
* To render the correct localized alt text dynamically, use image.altText[language].
Correct Selections:
* First blank: name[language]
* Second blank: "$when": "${stockLevel != 'OK'}"
* Third blank: image.altText[language]
* Adaptive Cards Templating
* Adaptive Card $when property
* Multilingual data in Azure Cognitive Search and Adaptive Cards

Explanation:
Detect the incoming language:
* Service: Speech to Text
* Reasoning: The Speech to Text service can transcribe spoken language into text and includes language identification capabilities, allowing the system to detect whether the caller is speaking French or English.
* Respond in the callers' own language:
* Service: Text to Speech
* Reasoning: The Text to Speech service can convert text responses into spoken language in the detected language (French or English), enabling the system to respond to callers in their own language.
* Detect the incoming language: Speech to Text
* Respond in the callers' own language: Text to Speech
Box 1: Text Analytics
The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
Box 2: Translator
Translator is a cloud-based neural machine translation service that is part of the Azure Cognitive Services family of REST APIs. Translator can be used with any operating system and powers many Microsoft products and services used by thousands of businesses worldwide to perform language translation and other language- related operations.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- language-detection
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/translator-overview

Entity linking in Azure AI Language identifies entities in text and links them to entries in a knowledge base such as Wikipedia, providing precisely the additional context and references you need. Custom NER extracts entities but doesn't link to Wikipedia; Content Safety moderates content; Key phrase extraction surfaces topics without external links.
Microsoft References:
* Entity linking overview (links entities to Wikipedia). Microsoft Learn
* How to call the Entity Linking API (returns entities with Wikipedia links)

"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.