Explanation:

Normalization involves eliminating relationships between database tables.
No
Normalization does not eliminate relationships. In fact, it often creates relationships between tables by splitting data into smaller, related tables.
Normalizing a database reduces data redundancy.
Yes
The primary goal of normalization is to minimize duplicate data and ensure that each fact is stored only once.
Normalization improves data integrity.
Yes
By reducing redundancy and ensuring data is stored in a structured manner, normalization enforces consistency and integrity across the database.
Correct Responses:
No
Yes
Yes
Microsoft References
Database normalization basics
Database design and normalization

Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-entity-recognition

Explanation:

To detect aggressive language (e.g., profanity/abuse) and sexually explicit content in text using the Content Moderator Text - Screen API:
classify = true
Enables machine-assisted text classification that returns category scores (e.g., sexually explicit/suggestive categories). This is required to identify sexual content.
language = < code > (for example, eng )
Specifies the language so profanity/abusive terms are detected correctly. The profanity detection in Text- Screen is language dependent.
autocorrect = true
Helps normalize intentionally misspelled or obfuscated words so that profanity or aggressive terms are still detected (e.g., "f@#$" # "fuck").
Other parameters in the blade are not necessary for this goal:
PII targets personal data extraction, not aggression/sexuality.
listId is for matching against custom blocklists.
Headers like Ocp-Apim-Subscription-Key and Content-Type are required to call the API, but they are not the content-analysis settings that control detection behavior asked for in this question.
Microsoft Azure AI References
Azure AI Content Moderator - Screen text API parameters (autocorrect, language, classify) and category outputs.
Azure AI Content Moderator - Text moderation concepts: profanity detection and classification categories.

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 Recap:
* Application: Flower species classification using Custom Vision.
* New images are received.
* Solution: Add the new images, and then use the Smart Labeler tool.
Analysis:
* The Smart Labeler tool in Custom Vision can help automatically generate suggested labels for newly uploaded images.
* This is a valid way to extend a classifier with new images.
* It doesn't complete the entire pipeline (you'd still retrain afterward), but using Smart Labeler does meet the goal of adding new species efficiently.
The answer: A. Yes