Free Microsoft AI-102 Practice Exam with Questions & Answers | Set: 5

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

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.

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.

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 .

Answer: No

Explanation

Identify the road signs and generate a short description of each road sign: Azure AI Vision is the most suitable choice for this task. It is designed for image analysis and can accurately detect and describe objects such as road signs in large datasets like your 100,000 images. This service is optimized for computer vision tasks and helps minimize costs by efficiently processing images at scale.

Analyze the descriptions to generate a report about the different types of road signs and how often each type occurred: Azure OpenAI GPT-4-Turbo is ideal for this action. It can process natural language descriptions, identify patterns, and generate a detailed report based on the frequency of different road sign types. This model is cost-effective for text analysis tasks and can handle the volume of data derived from the image descriptions while providing advanced language understanding.

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.

You are asked to build an app that translates speech from English into French, German, and Spanish using Azure AI Speech Translation.

Step 1 – Define target languages

The SpeechTranslationConfig object requires language codes (not full names like " French " , " German " , " Spanish " ).

Valid language codes:

French → " fr "

German → " de "

Spanish → " es "

Therefore, the correct syntax for the output languages is:

var languages = new List < string > { " fr " , " de " , " es " };

Step 2 – Choose the recognizer type

IntentRecognizer : Used for intent recognition with LUIS, not translation.

SpeakerRecognizer : Used for speaker identification/verification, not translation.

SpeechSynthesizer : Used for text-to-speech, not translation.

TranslationRecognizer : Used specifically for speech-to-speech or speech-to-text translation.

Therefore, the correct recognizer is TranslationRecognizer.

Correct Answer:

Output Languages: { " fr " , " de " , " es " }

Recognizer: TranslationRecognizer

Microsoft References

Speech Translation with Azure AI Speech

TranslationRecognizer Class (C# API)

Supported language codes

You are analyzing an image with Azure AI Content Safety using the following request payload:

{

" image " : {

" content " : " < base_64_string > "

},

" categories " : [

" Violence "

],

" outputType " : " EightSeverityLevels "

}

Key Points:

Category specified → " Violence "

The Content Safety API will check for violent content in the image.

Test image description → A simple circle.

This is not violent in any way.

Output type → " EightSeverityLevels "

This setting returns a floating-point severity score from 0.0 (no severity) up to 7.0 (highest severity).

Expected result:

Since the image is only a circle (non-violent), the violence severity level will be at the lowest possible value: 0.0

Correct Answer: B. 0.0

Microsoft References

Azure AI Content Safety – Image moderation

Content Safety severity levels