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

Comprehensive Detailed ExplanationThe question asks about testing a bot interactively on a local machine when using the Microsoft Bot Framework SDK.

Steps to test locally:

Build and run the bot

Compile the bot application code and run it locally (e.g., using Visual Studio or Node.js).

This exposes the bot’s endpoint on localhost (commonly http://localhost:3978/api/messages ).

Open the Bot Framework Emulator

The Bot Framework Emulator is a desktop app for testing and debugging bots built with the Bot Framework SDK.

It allows you to send messages to the bot and see the responses.

Connect to the bot endpoint

Use the Emulator to connect to the locally running bot by entering its endpoint URL.

Once connected, you can interact with the bot as if you were a real user.

Register the bot with the Azure Bot Service → Needed for cloud deployment, not local testing.

Open the Bot Framework Composer → Only needed if designing the bot with Composer, but this scenario uses the SDK directly.

Actions Not Required in This Case

Build and run the bot → Open the Bot Framework Emulator → Connect to the bot endpoint

Correct Sequences (multiple possible valid orders):

Test a bot locally with Bot Framework Emulator

Bot Framework SDK overview

Microsoft References

✅ Final Answer Sequence:

Build and run the bot

Open the Bot Framework Emulator

Connect to the bot endpoint

The requirements are:

Generate tags for uploaded images → This is an image analysis task.

Tags must be returned in the user’s preferred language → Must support localization of tags.

Languages required: English, French, and Spanish → Must support multiple languages.

Minimize development effort → Should use a prebuilt service rather than training a custom model.

Option Analysis:

A. Custom Vision image classification

Custom Vision requires training a model with your own dataset.

It outputs classification labels, but these are defined in English by the developer, and localization would require extra translation work.

More development effort, not ideal here.

B. Content Moderator Image Moderation

Used to detect adult/racy content, profanity, or unsafe imagery.

Does not generate descriptive image tags.

Not suitable.

C. Custom Translator

Used to train translation models for text.

Does not analyze or generate image tags.

Not applicable here.

D. Computer Vision Image Analysis

Provides prebuilt image tagging and description features.

Automatically generates descriptive tags for uploaded images.

Supports multiple languages (including English, French, and Spanish) for returning tags and descriptions.

Minimal development effort: simply call the Image Analysis API with the desired language parameter.

This exactly meets the requirements.

Correct Answer: D. Computer Vision Image Analysis

Azure AI Vision Image Analysis – Tags and Descriptions

Azure AI Vision supported languages

Quickstart: Analyze an image with the Computer Vision REST API

Microsoft References

semi-structured data

The statement is:

"Varying fields for each entity in a JSON document is an example of ______."

Relational data → Stored in structured tables with fixed schema (e.g., SQL database). Not applicable here.

Structured data → Has a strict schema (columns and types are predefined). JSON allows variation, so not structured.

Semi-structured data → Data that does not require a fixed schema, but still uses markers (tags, keys) to separate elements. JSON and XML are classic examples. ✅

Unstructured data → Data without schema or labeling (e.g., videos, images, free text). Not JSON.

Since JSON documents can have varying fields per entity (flexible schema), this is considered semi-structured data.

Correct Answer: semi-structured data

Structured, semi-structured, and unstructured data

JSON in Azure

Microsoft References

To allow users to cancel an in-progress transaction, you need an interruption mechanism.

In Bot Framework Composer, this is done using triggers such as an intent trigger or dialog trigger (e.g., a CancelIntent trigger that interrupts the current flow).

A language generator handles variations in bot responses.

A custom event is not required for simple cancellation.

A conversation activity handles messages or events but does not manage interruption logic.

Correct Answer: C

Text Analytics Cognitive Service could be used to quickly determine the public perception for a specific topic, event or brand.

Example: A NodeJS app which pulls Tweets from Twitter using the Twitter API based on a specified search term. Then pass these onto Text Analytics for sentiment scoring before storing the data and building a visualisation in PowerBI. The Architecture looked something like this:

You have a Computer Vision resource named contoso1 in West US region.

You must use this resource to resize an image using smart cropping.

Let’s analyze the key points:

Endpoint

Each Azure AI service resource has a regional endpoint.

Since the resource name is contoso1 and is deployed in West US, the correct endpoint will be:

https://contoso1.cognitiveservices.azure.com

The older projectoxford.ai and generic westus.api.cognitive.microsoft.com are legacy endpoints and are not recommended.

Therefore, the correct choice is https://contoso1.cognitiveservices.azure.com .

API Method

The task is to generate a different size of a product photo with smart cropping.

This is done using the Generate Thumbnail API in Computer Vision.

The path for this is:

/vision/v3.1/generateThumbnail

Other options like detect (for object detection) or areaOfInterest (for finding regions of interest) do not generate thumbnails.

Correct Answer:

Endpoint: https://contoso1.cognitiveservices.azure.com

API: /vision/v3.1/generateThumbnail

Computer Vision API reference - Generate Thumbnail

Computer Vision REST API documentation

Azure AI services endpoints

Microsoft References

Azure AI Agent Service projects are created and managed in Azure AI Foundry (ai.azure.com). Foundry provides the “project” workspace where you configure agents, connect tools (like speech and search), and use your Azure OpenAI deployments. Language Studio and Speech Studio are for the classic Language and Speech services respectively, and the Azure portal is used to create resources—not the agent project itself. The Microsoft docs explicitly state you start by creating an Azure AI Foundry project to build agents.

References

What is Azure AI Foundry Agent Service? – “To get started… create an Azure AI Foundry project.” Microsoft Learn

Create a project in Azure AI Foundry (projects organize agents, files, evaluations). Microsoft Learn

Quickstart: Create an agent in Azure AI Foundry Agent Service. Microsoft Learn

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

Knowledge store in Azure Cognitive Search

How to define a knowledge store

Microsoft References

To “recognize geographic locations” in Azure AI Search enrichment, add the EntityRecognitionSkill to your skillset. This built-in cognitive skill performs named entity recognition (NER) and extracts entities such as locations, people, and organizations from text.

EntityLinkingSkill links already-recognized entities to a knowledge base (e.g., Wikipedia) and is not required just to detect locations.

DocumentExtractionSkill parses file content/structure.

AzureOpenAIEmbeddingSkill creates vector embeddings, not entities.

Microsoft References

Azure AI Search – Entity recognition skill (extracts entities including locations).

Azure AI Search – Entity linking skill (disambiguates entities to a knowledge base; not for initial recognition).

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() { "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 Codestatic async Task TranslateSpeechAsync()

{

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

var lang = new List() { "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);

}

}

}

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

Second Blank: TranslationRecognizer

Final Answer (Answer Area Selections)

Speech Translation with Speech SDK

TranslationRecognizer class

Speech service supported languages

Microsoft References