You might want to manage the translation and localization for the language understanding content for your bot independently.
Translate command in the @microsoft/bf-lu library takes advantage of the Microsoft text translation API to automatically machine translate .lu files to one or more than 60+ languages supported by the Microsoft text translation cognitive service.
What is translated?
An .lu file and optionally translate Comments in the lu file
LU reference link texts
List of .lu files under a specific path.
Reference:
https://github.com/microsoft/botframework-cli/blob/main/packages/luis/docs/translate-command.md

"Customer-managed keys require an additional billable service, Azure Key Vault, which can be in a different region, but under the same subscription, as Azure Cognitive Search. Enabling CMK encryption will increase index size and degrade query performance." same document also lists Azure Key Vault as a requirement:
https://docs.microsoft.com/en-us/azure/search/search-security-overview#data-protection

You have an Azure AI Document Intelligence resource named AIDoc1 in the S0 tier.
You need to identify which files can be uploaded to Document Intelligence Studio for training a custom extraction model.
* Supported formats: PDF, TIFF, JPG, PNG, BMP.
* Maximum file size: 500 MB per document.
* Password-protected documents: Not supported.
* S0 tier limits: Same as above (50 pages per document limit also applies).
Now checking the given files:
* File1 (JPG, 400 MB, No password) # Supported (under 500 MB, allowed format).
* File2 (PDF, 250 MB, No password) # Supported.
* File3 (PNG, 180 MB, Password-protected) # Not supported (password locked).
* File4 (XLSX, 900 MB) # Not supported (format not supported, exceeds 500 MB).
* File5 (PDF, 160 MB, Password-protected) # Not supported (password locked).
The answer: C. File1, File2, and File4 only # Wait, check again: XLSX is not supported, so File4 is invalid. Only File1 and File2 are valid.
The answer: A. File1, and File2 only

To quickly prototype harmful-content detection (e.g., discriminatory language) with minimal development, you should provision an Azure AI Content Safety resource in the Azure portal and then try it in Content Safety Studio using "Moderate text content." Content Safety Studio lets you test text moderation, tune thresholds, and export sample code without writing much code up front. Creating an Azure OpenAI resource or using "Protected material detection for text" isn't required for abusive/hate moderation; "protected material" targets copyrighted/credential leakage scenarios, not general toxicity.
Microsoft References:
* Quickstart: Analyze text content (use Moderate text content in the studio; prerequisite is to create a Content Safety resource). Microsoft Learn
* Azure AI Content Safety overview

Explanation:

* In Custom Question Answering (part of Azure AI Language), if you want to define alternative words or spellings for a specific term (e.g., testing, trials, evaluate), you must use the alterations property.
* Each alteration object contains a list of alternative values for the same meaning.
* Example:
* {
* "alterations": [
* {
* "alterations": [
* "testing",
* "trials",
* "evaluate"
* ]
* }
* ]
* }
* phrases is used for pattern matching in question-answer pairs.
* synonyms is used for synonyms within LUIS (Language Understanding), not for QnA alterations.
* value is used inside synonyms definitions in some contexts but not here.
The JSON should be:
{
"alterations": [
{
"alterations": [
"testing",
"trials",
"evaluate"
]
}
]
}
Verified Answer:
* Left box: "alterations"
* Right box: "alterations"
Microsoft References:
* Manage alterations in Custom Question Answering (Azure AI Language)
* Authoring API reference for alterations