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:
Step 1: Create a new project and load sample documents
Create a new project. Projects store your configurations and settings.
Step 2: Label the sample documents
When you create or open a project, the main tag editor window opens.
Step 3: Train a custom model.
Finally, train a custom model.
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/label-tool

Explanation:
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
* 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 The function uses RecognizeEntities to identify entities in the text, which typically includes named entities like locations (e.g., Paris, Eiffel Tower). It then prints the text of each recognized entity. The input string "Our tour of Paris included a visit to the Eiffel Tower" contains the entities "Paris" and "Eiffel Tower," which will be broken down into individual words like "Paris," "Eiffel," and "Tower." However, common words like
"our" and "included" are not typically recognized as entities, and the function does not output all key phrases, only the recognized entities.

The scenario involves a Language Understanding (LUIS) or Conversational Language Understanding (CLU) model that has:
* An intent called FindContact (used for searching a contact list).
* Training phrases like:
* "Find contacts in London"
* "Who do I know in Seattle?"
* "Search for contacts in Ukraine"
The model must recognize locations (London, Seattle, Ukraine) inside the FindContact intent.
* The proposed solution is to create a new intent for location.
* This is not correct because:
* Intents represent what the user wants to do (the action, e.g., FindContact).
* Entities represent details within the user's utterance (the data, e.g., location).
* Here, the intent is already defined: FindContact.
* Location should be modeled as an entity (Location entity), not as a separate intent.
Creating a new intent for each location (London, Seattle, Ukraine) would fragment the model and make it harder to generalize. Instead, the model should capture location as a parameter of the single intent FindContact.
* Keep the existing FindContact intent.
* Add an entity for location (prebuilt GeographyV2 in LUIS or "Location" entity in CLU).
* Train the model so that London, Seattle, Ukraine are recognized as entity values inside the FindContact intent.
The answer: B. No
* Intents and entities in Language Understanding
* Prebuilt entity: GeographyV2

Azure AI Document Intelligence (Standard S0) enforces input constraints: images must be between 50×50 and 10,000×10,000 pixels and typical file size limits apply (images/PDFs well below hundreds of MB; an
800-MB PDF is far beyond supported sizes).
* File1.pdf (800 MB) - exceeds supported size limits # cannot be analyzed.
* File2.jpg (25×25 px) - below the 50×50 px minimum # cannot be analyzed.
* File3.tiff (5,000×5,000 px; 5 MB) - within pixel and size limits # can be analyzed.
References
* Document Intelligence service limits (file size/page and image dimension constraints).https://learn.
microsoft.com/azure/ai-services/document-intelligence/concept-limits
* Document Intelligence supported file types.https://learn.microsoft.com/azure/ai-services/document- intelligence/overview#supported-formats