Free Microsoft AI-102 Practice Exam with Questions & Answers | Set: 2

Comprehensive Detailed Explanation

The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.

Product Name Display

The product JSON stores localized names like " name " : { " en " : " ... " , " es " : " ... " , " pt " : " ... " } .

To dynamically render based on the current language, you must use name[language].

This ensures the product name shown in the card adapts automatically to the user’s preferred language.

Stock Level Warning

Business rules require warnings when stock is low or out of stock.

This means you display the stock warning only when stockLevel is not ' OK ' .

Therefore, the correct condition is:

" $when " : " ${stockLevel != ' OK ' } "

Image Alt Text for Accessibility

Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.

In the product JSON, " alttext " is also localized:

" altText " : { " en " : " Bicycle " , " es " : " Bicicleta " , " pt " : " Bicicleta " }

To render the correct localized alt text dynamically, use image.altText[language].

Correct Selections:

First blank: name[language]

Second blank: " $when " : " ${stockLevel != ' OK ' } "

Third blank: image.altText[language]

Microsoft References

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

Scenario: Support autocompletion and autosuggestion based on all product name variants.

A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.

POST /indexes/myxboxgames/docs/autocomplete?search & api-version=2020-06-30

{

" search " : " minecraf " ,

" suggesterName " : " sg "

}

B: In Azure Cognitive Search, typeahead or " search-as-you-type " is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for " Seattle " will have prefix combinations of " sea " , " seat " , " seatt " , and " seattl " to support typeahead.

F. Use the default standard Lucene analyzer ( " analyzer " : null) or a language analyzer (for example, " analyzer " : " en.Microsoft " ) on the field.

C:\Users\Waqas Shahid\Desktop\Mudassir\Untitled.jpg

To automatically delete blobs in Azure Blob Storage, you can configure a lifecycle management policy . This policy allows you to:

Transition blobs between hot, cool, and archive tiers.

Delete blobs automatically after a specified number of days.

Other options:

Change feed → Tracks all changes to blobs, but does not delete them.

Soft delete → Protects blobs from accidental deletion by allowing recovery, not automated deletion.

Archive storage → Storage tier for rarely accessed data, not a deletion mechanism.

Correct Answer: a lifecycle management policy

In the Bot Framework SDK, if you want to collect structured input step by step, you typically use a Waterfall dialog. Each step in the waterfall represents one piece of logic, such as asking for the size, toppings, or type of food item.

Prompts are used inside waterfall steps to actually ask the user for input (e.g., text prompt, choice prompt, date-time prompt).

Adaptive, Action, Input are concepts more relevant to Power Virtual Agents or Adaptive Dialogs (Composer), not the Bot Framework SDK question context.

Since the question specifically mentions Bot Framework SDK, the correct answer is Waterfall dialog + Prompts.

Correct Answer: C, D

Comprehensive Explanation

Speech-to-text converts the caller’s spoken French/German audio into text.

The text must then be translated to English.

Finally, Text-to-speech converts the translated text into spoken English for playback.

Therefore, the combination of Translator + Text-to-speech is required for the replay step.

Microsoft References

Speech-to-text in Azure AI Speech

Azure AI Translator

Text-to-speech in Azure AI Speech

Final Answer:

To capture messages → Speech-to-text

To replay messages → Text-to-speech and Translator

You are developing a speech translation service for recording lectures given in English (United Kingdom). The goal is to provide transcripts in multiple target languages: English, French, Spanish, and German.

Key requirements from the code snippet:

Source Language (Recognition Language):

config.SpeechRecognitionLanguage = " en-GB " ;

The input is English (UK).

Target Languages:

You need to define a list of supported target languages. Since English, French, Spanish, and German are supported, the correct syntax is:

var lang = new List < string > () { " en " , " fr " , " de " , " es " };

This uses ISO language codes expected by the Translator API ( " fr " = French, " de " = German, " es " = Spanish).

Recognizer Type:

Since this is speech translation, you must use:

using var recognizer = new TranslationRecognizer(config, audioConfig);

The TranslationRecognizer class is specifically for translating speech input into multiple languages.

Completed Code

static async Task TranslateSpeechAsync ()

{

var config = SpeechTranslationConfig.FromSubscription( " 69cad5cc-0ab3-4704-bdff-abf4aa07d85 " , " uksouth " );

var lang = new List < string > () { " en " , " fr " , " de " , " es " };

config.SpeechRecognitionLanguage = " en-GB " ;

lang.ForEach(config.AddTargetLanguage);

using var audioConfig = AudioConfig.FromDefaultMicrophoneInput();

using var recognizer = new TranslationRecognizer(config, audioConfig);

var result = await recognizer.RecognizeOnceAsync();

if (result.Reason == ResultReason.TranslatedSpeech)

{

foreach ( var element in result.Translations)

{

AppendToTranscriptFile(element.Value, element.Key);

}

}

}

Final Answer (Answer Area Selections)

First Blank: { " en " , " fr " , " de " , " es " }

Second Blank: TranslationRecognizer

Microsoft References

Speech Translation with Speech SDK

TranslationRecognizer class

Speech service supported languages

The question is about creating an Azure Cognitive Services resource programmatically with C#.

Key requirements from the question:

Free resource → This requires the F0 (Free) pricing tier.

West US region → Location should be " westus " .

Generate captions of images automatically → This requires the Computer Vision service.

Option Analysis

A. create_resource(client, " res1 " , " ComputerVision " , " F0 " , " westus " )

Uses " ComputerVision " kind → Correct, because Computer Vision provides the image captioning feature.

Tier is " F0 " → Correct, free tier.

Location " westus " → Correct.

This is the correct answer.

B. create_resource(client, " res1 " , " CustomVision.Prediction " , " F0 " , " westus " )

Custom Vision is for building and deploying custom image classifiers.

The Prediction resource is specifically for running inference, not general captioning.

Wrong service for the requirement.

C. create_resource(client, " res1 " , " ComputerVision " , " S0 " , " westus " )

" ComputerVision " is correct.

But " S0 " is a paid tier, not the required free tier.

Does not meet the requirement.

D. create_resource(client, " res1 " , " CustomVision.Prediction " , " S0 " , " westus " )

Wrong service (captioning is not handled by Custom Vision).

Wrong tier (paid instead of free).

Incorrect.

Correct Answer: A. create_resource(client, " res1 " , " ComputerVision " , " F0 " , " westus " )

Microsoft References

Azure AI Computer Vision documentation

Create a Cognitive Services resource in Azure

Computer Vision capabilities – Image analysis and captioning

When using the Speech SDK’s speech translation, a TranslationRecognitionResult includes a Reason property (type ResultReason ) indicating the outcome. For successful translation results, the reason is ResultReason.TranslatedSpeech . In that case, you can read the recognized source text from translationRecognitionResult.Text and iterate the translated outputs from translationRecognitionResult.Translations (a dictionary keyed by target language code).

RecognizingSpeech corresponds to interim hypotheses raised on the Recognizing event and is not the final result reason used here; NoMatch is the branch already handled for unrecognized audio.

Microsoft Azure AI References

Azure AI Speech SDK – Speech translation overview and TranslationRecognizer usage (C#): result handling with ResultReason.TranslatedSpeech , reading Text and Translations .

Speech SDK API reference – TranslationRecognitionResult ( Reason , Text , Translations ) and ResultReason enum (values include TranslatedSpeech , NoMatch , Canceled ).