Pass the Microsoft Azure AI Engineer Associate AI-102 Questions and answers with CertsForce

Azure AI Content Safety supports custom blocklists —dictionaries of terms/phrases you define to detect obscure or domain-specific offensive terms in user content. Creating a blocklist is the minimal-effort, built-in way to meet this requirement; you don’t need to build a classifier.

References

Create and use blocklists in Azure AI Content Safety.

https://learn.microsoft.com/azure/ai-services/content-safety/concepts/blocklists

Text moderation overview (using built-in classifiers with optional blocklists).

https://learn.microsoft.com/azure/ai-services/content-safety/concepts/text-moderation

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

Scenario Recap

You have a web app app1 running on VM1 in vnet1.

You plan to create an Azure Cognitive Search service (service1).

Goal: Ensure app1 can connect to service1 without routing over the public internet.

Proposed Solution

“You deploy service1 and a public endpoint to a new virtual network, and you configure Azure Private Link.”

Analysis

Azure Cognitive Search and Private Link:

Azure Cognitive Search supports Private Endpoints via Azure Private Link.

When you configure a private endpoint, the service is accessible privately over the virtual network and traffic does not go over the public internet.

You can still keep a public endpoint enabled optionally, but access from your VM can be restricted to the private endpoint.

Key Point: The solution specifies using a public endpoint AND a private link.

This still meets the requirement because Private Link ensures that VM1 in vnet1 communicates with service1 privately.

The existence of the public endpoint does not force traffic to go public — traffic from the private endpoint is routed over the VNet.

Conclusion

Yes, this solution meets the goal because Private Link provides secure, private connectivity to Cognitive Search, avoiding the public internet.

Correct Answer: A. Yes

Microsoft References

Secure access to Azure Cognitive Search with Private Link

Azure Private Link overview

You are building an application that:

Uses Azure Translator service to translate text.

The text comes from getTextToBeTranslated() .

The text can be in many languages.

Requirement: the content must remain within the Americas Azure geography.

Output: must translate text into a single language (English in this case).

Step 1 – Choosing the correct endpoint

The Translator service provides region-specific endpoints.

For Americas geography, you must use the North America (NAM) endpoint:

https: //api-nam.cognitive.microsofttranslator.com/translate

Other options like api-apc (Asia Pacific) or api-nam.../detect are incorrect because:

/detect is used to identify the source language, not translate.

/transliterate is for script conversion, not translation.

We need the /translate path.

Step 2 – Setting the correct query parameter

To specify the target language, you must use the to query string parameter. Example:

?t o=en

Options like from=en or suggestedFrom=en are not correct here, because:

The text can be in many languages → we don’t know the source language in advance.

Translator automatically detects the source if from is not specified.

We only need to define the target language.

Final Code Snippet (corrected)

var endpoint = " https://api-nam.cognitive.microsofttranslator.com/translate " ;

var apiKey = " YOUR_SUBSCRIPTION_KEY " ;

var text = getTextToBeTranslated();

var body = " [{ ' Text ' : ' " + text + " ' }] " ;

var client = new HttpClient();

client.DefaultRequestHeaders.Add( " Ocp-Apim-Subscription-Key " , apiKey);

var uri = endpoint + " ?to=en " ;

HttpResponseMessage response;

var content = new StringContent(body, Encoding.UTF8, " application/json " );

response = await client.PutAsync(uri, content);

Correct Answer

Endpoint: https://api-nam.cognitive.microsofttranslator.com/translate

URI: ?to=en

Microsoft References

Translator v3.0 Reference

Region-specific Translator endpoints

Text Extraction: b) Computer Vision

Computer Vision is a service that can analyze images and extract text from them using optical character recognition (OCR) or read API1. OCR can detect and extract printed or handwritten text from images in various languages and formats, such as PDF, TIFF, or JPEG2. Read API can perform asynchronous batch processing of text from multiple images, and can also handle text in tables, mixed languages, and rotated text3.

Profane Language Detection: c) Content Moderator

Content Moderator is a service that can detect and filter out potentially offensive or unwanted content in text, images, and videos. For text, Content Moderator can identify profane or abusive language, personal data, and custom terms that you define. You can also use Content Moderator to review and moderate the content manually or automatically using workflows.

Comprehensive Detailed Explanation

The screenshot shows a Bot Framework Composer dialog that collects user input:

user.name is an entity.

False.

user.name is a property in bot state (a variable), not an entity.

Entities are part of LUIS/NLU models (e.g., PersonName, DateTime).

The dialog asks for a user name and a user age and assigns appropriate values to the user.name and user.age properties.

True.

The dialog explicitly has:

A Bot Asks (Text) step → assigns user’s response to user.name .

A Bot Asks (Number) step → assigns response to user.age .

So the values are stored in those properties.

The chatbot attempts to take the first non-null entity value for userName or personName and assigns the value to user.name.

True.

On the right-hand side, the Value expression is:

=coalesce(@userName, @personName)

coalesce returns the first non-null value.

This means if userName entity is available, it is used; otherwise, personName is used.

That result is assigned to user.name .

Correct Answers:

No

Yes

Yes

Microsoft References

Bot Framework Composer - Manage variables and properties

Bot Framework Composer - Recognizers and entities

Coalesce function in Composer expressions

When creating a voice talent profile for Custom Neural Voice in Azure Cognitive Services Speech, you must upload a consent recording .

The consent recording is a .wav or .mp3 file where the voice talent explicitly states that they consent to the creation of a synthetic version of their voice.

Audio samples and transcripts (training data) are uploaded later for training the model.

The profile itself requires the consent recording to comply with Microsoft’s responsible AI principles.

Comprehensive Detailed Explanation

To move a Question Answering project to a different Azure region, you migrate the project data and then re-deploy it in the target region:

Export the existing project (source region).

In Language Studio (Question Answering), you first export the project. This produces a package (JSON/ZIP) containing your knowledge bases, sources, synonyms, and settings. Exporting is the supported way to move a project between resources/regions.

Import the project into the new Language service instance (target region).

In the target region’s Language service instance, use Language Studio to import the previously exported file. This recreates the project (KBs and configuration) under the new resource.

Train and publish from the new instance.

After import, you must train (build) and then publish the project so that an endpoint is created under the new regional resource. Clients can then point to the new endpoint and key.

Actions like “enable custom text classification” or “regenerate the keys” are not required for moving a Question Answering project, and training/publishing from the original instance won’t deploy to the target region.

Microsoft Azure AI Solution References

Export and import Question Answering projects in Language Studio (move between resources/regions):

Microsoft Learn – Export and import projects (Question Answering) .

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/export-import-project

Train and publish a Question Answering project:

Microsoft Learn – Create, train, and publish projects (Question Answering) .

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/create-test-deploy

Language service overview and regional deployment concepts:

Microsoft Learn – What is Azure AI Language?

https://learn.microsoft.com/azure/ai-services/language-service/overview

Can search content in different formats, including video

Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.

Keywords extraction: Extracts keywords from speech and visual text.

Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).

Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.

Artifacts: Extracts rich set of " next level of details " artifacts for each of the models.

Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.