Free Microsoft AI-102 Practice Exam with Questions & Answers

Box 1: Adaptive card

Box 2: an image

You are using Azure Cognitive Search, where access is controlled by:

Admin keys: Provide full read/write control (never given to apps).

Query keys: Provide read-only access, meant for client apps.

Here, the query key has been compromised. You want to minimize downtime and ensure continued read-only access.

First, generate a new query key from the Azure portal or via REST API.

This allows you to continue providing read-only access to authorized apps.

Update the application configuration so that it authenticates requests with the new query key.

This ensures uninterrupted service while preparing to retire the compromised key.

Once the app is using the new key successfully, revoke the compromised query key.

This prevents further unauthorized usage.

Step 1 – Add a new query keyStep 2 – Change the app to use the new keyStep 3 – Delete the compromised key

Regenerate primary/secondary admin key: Not necessary since only the query key is compromised, and admin keys give full control.

Change app to use secondary admin key: Insecure (admin keys should never be exposed to client apps).

Why not other options?

Add a new query key.

Change the app to use the new key.

Delete the compromised key.

Correct Sequence

Manage admin and query keys in Azure Cognitive Search

Create and use query keys

Microsoft References

You want to prevent users from sharing sexually explicit images and minimize development effort.

Azure AI Content Safety Studio provides a no-code, browser-based experience to test and tune image moderation using the Content Safety service. It supports out-of-the-box classification for Sexual, Hate, Violence, and Self-harm categories, lets you adjust severity thresholds, review results, and export settings or sample code with minimal engineering work.

Other options:

A. Visual Studio – an IDE, not a moderation service.

B. Vision Studio in Azure AI Vision – focuses on vision tasks (OCR, image analysis). For policy/compliance moderation of explicit content, Content Safety is the correct service.

D. Azure AI Studio – general hub for building generative AI apps; not the specialized moderation experience.

Microsoft References

Azure AI Content Safety overview and image moderation concepts.

Content Safety Studio quickstart and scenarios.

Content categories (Sexual, Hate, Violence, Self-harm).

The interface must collect structured input: start date, end date, PTO days.

A waterfall dialog is designed for step-by-step data gathering. Each step asks one question and collects one piece of information, minimizing complexity.

Skill dialog is for invoking another bot.

Adaptive dialog handles interruptions and dynamic flow (more complex).

Component dialog organizes dialogs but does not simplify sequential data collection.

Correct Answer: B

For a social media messaging app that must identify the language of text messages in real time, the most direct SDK is the Azure AI Translator – Text Translation SDK. Its client libraries (e.g., Azure.AI.Translation.Text in .NET) include a language detection operation that returns the detected language code and a confidence score, which is exactly what you need for real-time text scenarios.

Other options are not a fit:

Microsoft.CognitiveServices.Speech is the Speech SDK for speech-to-text, TTS, and speech translation (audio), not text-only language detection.

Azure.AI.Translation.Document targets asynchronous document translation workflows, not short text message detection. Microsoft Learn

Azure.AI.Translation.Speech isn’t the package used; speech translation is covered by the Speech SDK package above. Microsoft Learn

Microsoft References

Text Translation SDK overview (supports language detection): Azure AI Translator. Microsoft Learn

DetectedLanguage in Azure.AI.Translation.Text (returned by detect operation). Microsoft Learn

Quickstart: Translator text client libraries. Microsoft Learn

Install/use the Speech SDK (contrast with text scenario). Microsoft Learn

Document Translation SDK overview (not applicable to short text detection). Microsoft Learn

The requirement is:

"Measure the public perception of your brand on social media using natural language processing (NLP)."

A. Content Moderator

Designed to filter offensive or inappropriate content in text, images, and videos.

Not suitable for sentiment analysis or measuring perception.

B. Form Recognizer

Extracts structured data from documents (invoices, receipts, forms).

Not used for analyzing social media posts.

C. Computer Vision

Analyzes images and videos for object detection, image classification, OCR, etc.

Not used for text-based sentiment analysis.

D. Language service ✅

Provides natural language processing (NLP) capabilities such as sentiment analysis, opinion mining, key phrase extraction, entity recognition, and language detection.

Perfect fit for analyzing public perception from social media posts.

The task is to process incoming emails and route them to French or English support teams. This means we need to detect the language of each email.

Let’s break it down:

Which Cognitive Service is needed?

Azure Text Analytics (part of Azure AI Language) provides a Language Detection API.

The Translator service is used for translation, not just detecting the original language. Since the requirement is only to decide whether to send emails to French or English teams, we just need to detect the language.

Endpoint selection

Text Analytics uses regional endpoints such as:https:// .api.cognitive.microsoft.com/

For example: eastus.api.cognitive.microsoft.com is valid.

api.cognitivemicrosofttranslator.com → incorrect (this is for Translator service).

portal.azure.com → Azure portal, not an API endpoint.

API Path selection

To detect language, you call:/text/analytics/v3.1/languages

/text/analytics/v3.1/entities/recognition/general → entity recognition, not language.

/translator/text/v3.0/... → translation, not language detection.

Correct Answer Combination:

eastus.api.cognitive.microsoft.com

/text/analytics/v3.1/languages

Text Analytics Language Detection API

Text Analytics REST API v3.1

Microsoft References

The question requires enabling the bot to respond to spoken requests with minimal effort. Among the options:

Cortana integration automatically adds speech input/output capabilities to the bot.

Direct Line Speech is another option but requires additional setup and coding, so not the minimal effort solution.

Microsoft Teams is primarily for text-based interactions (though can do voice calls, but not direct bot speech input).

Azure function + Speech would require more development overhead.

Thus, Cortana integration provides the simplest path.

When testing a bot with the Bot Framework Emulator, especially if the bot uses authentication (OAuth), you need the Emulator to mimic real-world authentication flows. To do that:

Use version 1.0 authentication tokens

Ensures that authentication tokens are generated in the same format as Bot Framework v1.0, which is compatible with most OAuth flows.

Use a sign-in verification code for OAuthCard

Prompts the user with a verification code during sign-in flows, ensuring the emulator behaves like a real client app (e.g., mobile or Teams).

This is required to test sign-in prompts and enforce credential entry.

Use your own user ID to communicate with the bot

Ensures that the Emulator identifies you uniquely and triggers authentication properly.

Without this, the bot may treat sessions generically, and authentication prompts might not behave as expected.

Why not the others?

Application Updates (automatically download/install) → Related to emulator updates, not authentication.

Data Collection → Only for telemetry; does not affect authentication.

Ngrok settings → Used for tunneling remote bot connections; unrelated to credential prompts.

Correct Answer Selections:

Use version 1.0 authentication tokens

Use a sign-in verification code for OAuthCard

Use your own user ID to communicate with the bot

Bot Framework Emulator - Authentication

Testing bots with Bot Framework Emulator

Microsoft References

Comprehensive Detailed ExplanationA custom neural voice (professional voice) project in Azure AI Speech requires that you follow Responsible AI and consent steps before any model training. The correct workflow in Speech Studio / Azure AI Foundry is:

Create a custom voice project – This is the container that will hold the voice talent consent, your training datasets, models, and deployments. Each project is scoped to a locale and other attributes. Microsoft Learn

Collect and upload consent – Microsoft requires explicit authorization from the voice talent. In practice, you add the voice talent consent to the project. The platform requires the verbal consent recording (WAV); many guidance and exam items also include the signed consent document (PDF) as part of consent capture to meet Responsible AI expectations. The consent step is performed before uploading training data.

Upload the voice talent’s verbal consent as a WAV file – The consent audio verifies that the person who granted consent is the same as the speaker in your training data. The documentation shows consent is a recorded statement uploaded to the project. Microsoft Learn

Analyze the quality of the audio data and fix issues – After data upload, Speech Studio runs validation and provides a per-utterance analysis (format checks, sampling rate, script match, pronunciation/noise scoring). You must resolve issues and run Analyze data before the dataset can be used for training. Microsoft Learn

Train the model using a neural training method – Start model training from the project and choose Neural as the training type for custom neural voice. Microsoft Learn

Why not “upload speech samples as WMA/MP3 files”?

For the standard, recommended Individual utterances + matching transcript data type used to fine-tune a professional neural voice, the audio files must be WAV/RIFF PCM; the docs explicitly reference “.wav” throughout data checks and corrections. WMA isn’t supported, and MP3 isn’t accepted for this core data type. (Long audio / audio-only workflows can accept MP3 for preprocessing, but the canonical training dataset for custom neural voice is WAV.)

Create a project for professional voice (Speech Studio/Azure AI Foundry): Project contains consent, datasets, models, endpoints. Microsoft Learn

Add voice talent consent to the professional voice project: Upload the recorded consent statement for the voice talent. Microsoft Learn

Professional voice fine-tuning data – required formats: Individual utterances + transcript require WAV/RIFF PCM; guidance on sample rate and transcript format. Microsoft Learn

Add a professional voice training dataset & resolve data issues: Data validation, pronunciation/noise checks, and the Analyze data step prior to training. Microsoft Learn

Train your professional voice model: Start training and select Neural method. Microsoft Learn

Microsoft Azure AI References