AI-102 Exam Dumps - Microsoft Azure AI Engineer Associate Questions and Answers

https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/named-entity-recognition/overview Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.

Data visualization’s primary purpose is to make data understandable and meaningful through visuals such as charts, dashboards, and graphs.

It helps to communicate significance (option A) and show patterns, correlations, and trends over time (option B).

C. Implement machine learning to predict future values → Not visualization, but rather ML/AI.

D. Enforce business logic across reports → Business logic is handled in data modeling or ETL, not visualization.

Correct Answers: A and B

The provided code uses the Azure AI Language – Text Analytics client to call the ExtractKeyPhrases method.

The method ExtractKeyPhrases(text) identifies the most important words and phrases in the input text.

The input string is:

" the quick brown fox jumps over the lazy dog "

The ExtractKeyPhrases API removes stopwords such as " the, " " over, " and " jumps " and extracts only meaningful keywords.

Therefore, the returned list of key phrases will include:

" Quick brown fox "

" Lazy dog "

When printed, the console output after " Key phrases: " will show:

Quick brown fox

Lazy dog

This matches option C (Quick brown fox lazy dog), since the key phrases are concatenated as output lines.

Correct Answer: C. Quick brown fox lazy dog

Microsoft References

Azure AI Language – Key phrase extraction

TextAnalyticsClient.ExtractKeyPhrases method (Azure.AI.TextAnalytics)

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.

Comprehensive Detailed Explanation

A 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.)

Microsoft Azure AI References

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 Le arn

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

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

Microsoft References

Bot Framework Emulator - Authentication

Testing bots with Bot Framework Emulator

To deploy the bot to Azure, you should run the following three Azure CLI commands in sequence:

az deployment group create This command will create the Azure resources for your bot using an ARM template and a parameters file. You need to specify the resource group name, the template file path, and the parameters file path. For example:

az deployment group create --resource-group myResourceGroup --template-file “deploymentTemplates\template-with-preexisting-rg.json” --parameters “deploymentTemplates\parameters-for-template-BotApp-with-rg.json”

This command will also output the app ID and password of your bot, which you will need for the next command1.

az webapp deployment source config-zip This command will deploy your bot code to the app service that you created in the previous step. You need to specify the resource group name, the app service name, and the zip file path of your bot code. For example:

az webapp deployment source config-zip --resource-group myResourceGroup --name myBotAppService --src “code.zip”

This command will also output the URL of your bot endpoint, which you will need for the next command2.

az ad app update This command will update your bot registration with the endpoint URL of your bot. You need to specify the app ID of your bot and the endpoint URL. For example:

az ad app update --id myBotAppId --set replyUrls=“https://myBotAppService.azurewebsites.net/api/messages”

This command will complete the deployment process and make your bot ready to be tested3.