Explanation:

Box 1: AddPhraseListAsync
Example: Add phraselist feature
var phraselistId = await client.Features.AddPhraseListAsync(appId, versionId, new PhraselistCreateObject
{
EnabledForAllModels = false,
IsExchangeable = true,
Name = "QuantityPhraselist",
Phrases = "few,more,extra"
});
Box 2: PhraselistCreateObject
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api

Explanation:

To mask (redact) phone numbers and email addresses in text using Azure AI Language, you should call the PII entity recognition operation and then print the redacted text that the service returns. In the SDKs, this corresponds to calling RecognizePiiEntities and reading the RedactedText from the result object. The PII feature automatically detects categories such as PhoneNumber and Email and returns a redacted version of the input (masked) in the redactedText/RedactedText field. This is exactly what the code snippet's "Masked Text" output is asking for.
Therefore:
Use RecognizePiiEntities to detect and redact PII in the document.
Output RedactedText to print the masked string.
Microsoft References
How-to: Detect and redact PII in text (overview and redaction behavior). Microsoft Learn Quickstart: Detect Personally Identifiable Information (shows PII detection and redacted output). Microsoft Learn NET reference: PiiEntityCollection.RedactedText property ("Gets the text of the input document with all PII redacted"). Microsoft Learn Python client overview: recognize_pii_entities operation for PII detection. Microsoft Learn

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.
When no good match is found by the ranker, the confidence score of 0.0 or "None" is returned and the default response is "No good match found in the KB". You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.
Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/best-practices

Defender for Cloud Apps (A): Focused on monitoring SaaS app usage and cloud security, not image content moderation # # Custom Vision (B): Can classify images, but requires building/training your own model # more development effort # # Azure AI Vision (C): Can analyze images, including adult/racy content detection, but Content Safety is now the preferred service for moderation. Still valid # # Azure AI Content Safety (D): Specifically designed for detecting and blocking harmful/inappropriate content in text and images # # Document Intelligence (E): Extracts text/structure from documents, not relevant here # # The Answer:
C). Azure AI Vision and D. Azure AI Content Safety
Reference:
Azure AI Content Safety overview
Azure AI Vision - image moderation

For a table projection in a knowledge store (skillset definition), the table node must define:
tableName : the table to project into (physical expression).
generatedKeyName : the name of the system-generated key column.
source : an object-shaping expression (projection path) describing what document content flows into the table.
dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.
References
Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.