Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) - AI-102 Korean Free Exam Questions

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 Comprehensive Detailed Explanation Let's examine the function:
static void GetKeyWords ( TextAnalyticsClient textAnalyticsClient, string text)
{
var response = textAnalyticsClient.RecognizeEntities(text);
Console.WriteLine( " Key words: " );
foreach (CategorizedEntity entity in response.Value)
{
Console.WriteLine( $ " {entity.Text} " );
}
}
The function uses RecognizeEntities from the Azure Text Analytics SDK.
RecognizeEntities detects and categorizes named entities in text (such as locations, people, organizations, dates, etc.).
It does not extract all words or key phrases.
For extracting key phrases, you would use ExtractKeyPhrases , not RecognizeEntities .
Evaluating the statements:
" The output will include the following words: our and included. "
These are common words, not entities. They will not appear.
The answer: No
" The output will include the following words: Paris, Eiffel, and Tower. " These are named entities (location, landmark). They will be recognized and returned.
The answer: Yes
" The function will output all the key phrases from the input string to the console. " No. The function extracts entities, not key phrases. Key phrases would require ExtractKeyPhrases .
The answer: No

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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 build narration for video training content using Custom Neural Voice, we must know where each step is done:
Creating a Custom Neural Voice
A Custom Neural Voice is a text-to-speech (TTS) feature within Azure AI Speech.
It allows you to create a unique, natural-sounding synthetic voice.
The correct tool to build and configure a custom voice is the Speech Studio portal.
Not the Language Understanding portal (LUIS), Azure portal directly, or Bot Framework Composer.
Generating narration
Narration requires converting written text into speech.
This is done via the Text-to-Speech service in Azure AI Speech.
Other options:
Language Understanding # For intent/utterance processing, not narration.
Speaker Recognition # For identifying/authenticating speakers, not narration.
Speech-to-text # Converts spoken words to text (the opposite direction).
Thus:
Custom Neural Voice # Speech Studio portal
Narration # Text-to-speech
Correct Answers:
Custom neural voice: The Speech Studio portal
Narration: Text-to-speech
Microsoft References
Custom Neural Voice in Speech Studio
Text-to-Speech overview

Explanation:

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Explanation: Only visible for Actual4test members. You can sign-up / login (it's free).

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 Microsoft References Custom Named Entity Recognition in Azure AI Language PII detection in Azure AI Language