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

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

Azure AI Video Indexer (VI) imposes limits on both file duration and file size:

Duration: Up to 6 hours for all presets; Basic Audio preset supports up to 12 hours.

Size: When uploading from your device, the limit is 2 GB (uploading by URL allows up to 30 GB).

Formats: MP4, AVI (video) and MP3 (audio codec/file) are supported.

Evaluating each file:

File1 – MP4, 34 mins, 1,500 MB: Within 6-hour duration and under 2 GB → Supported.

File2 – AVI, 500 mins (≈8h20m), 1,700 MB: Exceeds the 6-hour limit → Not supported.

File3 – MP3, 300 mins (5h), 980 MB: Under 6 hours (and also under the 12-hour Basic Audio limit) and under 2 GB → Supported.

File4 – MP4, 350 mins (≈5h50m), 2,800 MB: Duration is under 6 hours but size exceeds 2 GB for device uploads → Not supported (unless uploaded by URL, which the question doesn’t state).

Therefore, the files you can upload and analyze (under standard device upload constraints) are File1 and File3 only.

Microsoft References

Support matrix & limits — file duration (6h for all presets; 12h for Basic Audio), device upload size limit 2 GB; supported formats (MP4, AVI, WAV, etc.) and audio codecs including MP3. Microsoft Learn

Release notes — increase of duration limit from 4h to 6h (and 12h for Basic Audio). Microsoft Learn

Upload guidance — device upload 2 GB vs 30 GB via URL.

API Endpoint Selection

The constraint states that the content of the text must remain within the Americas Azure geography .

" api.cognitive.microsofttranslator.com " : This is the global endpoint , which doesn ' t guarantee data residency in a specific geography.

" api-apc.cognitive.microsofttranslator.com " : This endpoint refers to the Asia Pacific geography .

" api-nam.cognitive.microsofttranslator.com " : This endpoint refers to the North and South America (NAM) geography .

Therefore, to meet the data residency requirement for the Americas, you must select " api-nam.cognitive.microsofttranslator.com " .

2. Path/Query String Selection

The requirement is to translate the text to a single language . The input text can be in " one of many languages, " meaning the source language isn ' t fixed and should be detected automatically. The language to translate to is implied to be English ( en ) based on the options available and common translation scenarios.

" /translate?from=en " : This is incorrect because it specifies the source language as English, which contradicts the " text can be in one of many languages " requirement (implying automatic source language detection).

" /translate?suggestedFrom=en " : This is not the standard way to initiate a translation request and seems to suggest a source language, which is unnecessary if auto-detection is desired/possible.

" /translate?to=en " : This is the correct and standard way to request a translation where the source language is automatically detected , and the target language is English ( en ) . This fulfills the translation requirement.

" /detect?to=en " / " /detect?from=en " : These endpoints are used for language detection and not for the translation itself. The prompt explicitly asks to develop code to translate the text.

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.

When you want to debug a chatbot endpoint remotely that was built using the Microsoft Bot Framework, you need tools that allow you to test and tunnel your bot service to your local development environment.

1. Bot Framework Emulator

This is the official testing and debugging tool for bots built with the Microsoft Bot Framework.

It allows developers to:

Connect to a bot running locally or remotely.

Inspect messages sent and received.

Debug conversations and payloads in detail.

Required for local testing of the bot.

Correct.

2. ngrok

Ngrok is a tunneling tool that exposes your local development server (localhost) to the internet via a secure tunnel.

This is essential when debugging remotely because the Bot Framework Service (Azure Bot Service) requires a publicly accessible HTTPS endpoint for communication.

With ngrok, your local bot endpoint (e.g., http://localhost:3978/api/messages ) is mapped to a secure public endpoint (e.g., https://randomstring.ngrok.io/api/messages ).

Correct.

Incorrect Options

A. Fiddler

A web debugging proxy. Useful for monitoring traffic, but not required for Bot Framework remote debugging.

Incorrect.

B. Bot Framework Composer

A tool for building conversational bots visually. Not required for debugging an existing endpoint.

Incorrect.

D. Bot Framework CLI

Used for bot project management and configuration. Not specifically required for remote debugging endpoints.

Incorrect.

F. nginx

A web server/reverse proxy. Not needed for this scenario.

Incorrect.

Correct Answer: C. Bot Framework Emulator, E. ngrok

Microsoft References

Debug a bot locally using Bot Framework Emulator

Use ngrok with Bot Framework Emulator

To detect hateful content with Azure AI Foundry Content Safety – Text , you should read the category analysis results returned by the analyze_text call. The response exposes a collection named categories_analysis, where each item contains the analyzed category (e.g., TextCategory.HATE) and its severity level.

Hence:

Iterate over response.categories_analysis, filter the item where item.category == TextCategory.HATE.

Print the severity using hate_result.severity.

This uses category analysis (not blocklist matches), which is the correct mechanism for detecting hate content with a severity score.

Microsoft References

Azure AI Content Safety – Analyze text (Python quickstart/samples): iterate response.categories_analysis, check TextCategory.HATE, read item.severity.

Azure AI Content Safety – Text categories and severity levels: explains categories (Hate, Violence, Sexual, Self-harm) and severity output.

The code creates a SpeechConfig and an AudioOutputConfig with filename= " path/to/file.wav " , then constructs a SpeechSynthesizer and calls speak_text_async(input) .

Existing file behavior: AudioOutputConfig with a filename directs synthesis output to that file path; the documentation emphasizes that it specifies the output file and that the parent directory must already exist. It does not require a unique filename (i.e., it does not fail purely because a file exists). In practice, the SDK writes to the specified path. Therefore, the statement claiming failure if a file already exists is No.

Voice source: The filename in AudioOutputConfig is output, not input. To choose a voice, you set speech_config.speech_synthesis_voice_name or use SSML; the WAV file isn’t “sampled” to create a voice. Hence this statement is No. Microsoft Learn

File generation: Using AudioOutputConfig(filename=...) with SpeechSynthesizer and speak_text_async(...) writes synthesized audio to that file. Many samples show the same pattern (often with .get() to wait for completion). So the function’s purpose is to generate an audio file from the input text—Yes.

Note: In production, call .get() (or await) on speak_text_async(...) so the program waits for synthesis to complete before exiting. Microsoft Learn

References (Microsoft Azure AI Speech)

How to synthesize speech from text (create AudioOutputConfig to write to a file; synthesize with speak_text_async ).

Python AudioConfig class (audio output can be a speaker, audio file (WAV), or stream). Microsoft Learn

NET AudioConfig.FromWavFileOutput (specifies output file; parent directory must already exist). Microsoft Learn

Azure Q & A example (Python: speak_text_async(...).get() usage). Microsoft Learn

Comprehensive Detailed Explanation

In the Microsoft Bot Framework Composer, there are four main property scopes for storing variables or properties:

dialog scope

Lives only for the lifetime of the dialog.

Once the dialog ends, properties stored in the dialog scope are automatically cleared.

Best suited for temporary values such as user inputs collected during a dialog, like “name” in this case.

user scope

Persists across all conversations with the user.

Data stored here remains available even after dialogs end, making it unsuitable if you want to dispose of the property at the end of the dialog.

conversation scope

Persists throughout the lifetime of a single conversation (from start to end).

Useful for maintaining state across multiple dialogs in the same conversation, but still not automatically disposed of when a dialog ends.

turn (curn in the options is likely a typo)

Lives only for a single turn (a single exchange of user and bot messages).

Cleared at the end of the turn, which is too short-lived for this requirement.

Applying to the Question

Requirement: “Ensure that you can dispose of the property when the last active dialog ends.”

That means the property should exist only during the dialog execution and then be discarded.

Correct scope for this scenario is dialog.

Correct Answer: A. dialog

Microsoft References

Manage properties in Bot Framework Composer

Scopes in Bot Framework Composer

Bot Framework Composer memory model

Comprehensive Detailed Explanation

In an Azure Cognitive Search knowledge store, you can project enriched content into three main forms:

File projection → For storing binary or unstructured documents (e.g., scanned PDFs, images).

Object projection → For storing JSON objects (hierarchical/unstructured JSON data).

Table projection → For storing structured, tabular results (relational data).

Case 1: JSON data

JSON is already structured/unstructured textual data.

The right projection type for JSON in the knowledge store is Object projection.

Case 2: Scanned data

Scanned PDF/image data is binary file-based content.

The right projection type is File projection, so you can store the original scanned file along with extracted text.

Final Answer (Answer Area)

JSON data: Object projection

Scanned data: File projection

Microsoft References

Azure Cognitive Search knowledge store projections

Object vs File vs Table projections

In Azure Cognitive Search, a knowledge store is an optional capability that allows you to persist the enriched data from a skillset (enrichment pipeline) into an Azure Storage account. This is often used when you want to further analyze or visualize enriched content, e.g., sentiment analysis results from social media posts.

When defining a knowledge store, you need:

storageConnectionString

This is required so the enrichment pipeline can connect to your Azure Storage account.

Without it, the knowledge store cannot persist any data.

storageContainer

This specifies the container in Blob Storage where enriched documents or projections will be stored.

This allows you to persist the original documents and enriched outputs such as sentiment analysis scores.

Other options (not correct in this case):

tables → This is a projection type used within a knowledge store to structure output in Table Storage, but it’s not required in the definition itself.

files → Another projection type for storing enriched documents as files in Blob Storage, but again, not a required field in the definition itself.

objects → A projection type used for representing enriched documents as JSON objects in Blob Storage.

Thus, while tables, files, and objects are used inside projections within a knowledge store, the definition itself always requires a storage connection string and storage container.

Correct Answer:

A. storageContainer

C. storageConnectionString

Microsoft References

Knowledge store in Azure Cognitive Search

How to define a knowledge store