Microsoft AI-102 - Designing and Implementing a Microsoft Azure AI Solution

A data warehouse is designed for analytical workloads (OLAP), not transactional workloads (OLTP).

It integrates data from multiple sources and supports complex queries, aggregations, and reporting for decision-making.

A. storage for transactional LOB apps → describes OLTP (e.g., SQL Server, Azure SQL Database), not a data warehouse.

B. transformation services → describes ETL/ELT pipelines (e.g., Data Factory), not the warehouse itself.

C. read-only storage of historical data → partially true but too narrow; the key feature is analytics, not just storage.

D. answers to complex queries across multiple sources → Correct and complete definition.

Correct Answer: D

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.

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

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.

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.

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:39 78/api/messages) is mapped to a secure public endpoint (e.g., https://randomstring.ngrok.io/api/messages ).

Correct.

1. Bot Framework Emulator2. ngrok

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.

Incorrect Options

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

Debug a bot locally using Bot Framework Emulator

Use ngrok with Bot Framework Emulator

Microsoft References

You are asked to build an app that translates speech from English into French, German, and Spanish using Azure AI Speech Translation.

Step 1 – Define target languages

The SpeechTranslationConfig object requires language codes (not full names like "French", "German", "Spanish").

Valid language codes:

French → "fr"

German → "de"

Spanish → "es"

Therefore, the correct syntax for the output languages is:

var languages = new List { "fr", "de", "es" };

Step 2 – Choose the recognizer type

IntentRecognizer: Used for intent recognition with LUIS, not translation.

SpeakerRecognizer: Used for speaker identification/verification, not translation.

SpeechSynthesizer: Used for text-to-speech, not translation.

TranslationRecognizer: Used specifically for speech-to-speech or speech-to-text translation.

Therefore, the correct recognizer is TranslationRecognizer.

Correct Answer:

Output Languages: {"fr","de","es"}

Recognizer: TranslationRecognizer

Speech Translation with Azure AI Speech

TranslationRecognizer Class (C# API)

Supported language codes

Microsoft References

For a phone-style bot that listens to customers and replies with spoken output, you use two core Azure AI Speech SDK objects:

SpeechRecognizer performs speech-to-text (STT), turning the caller’s audio into text that your app can use to query the orders database. This minimizes effort because it handles audio capture/streaming, language models, and returns recognized text events/results directly.

SpeechSynthesizer performs text-to-speech (TTS), converting the retrieved order status text into natural-sounding audio to play back to the customer. It supports neural voices and simple calls like SpeakTextAsync().

Other options are not appropriate here:

TranslationRecognizer is for real-time speech translation, not needed for simple STT.

VoiceProfileClient is for speaker verification/identification, not for recognition or synthesis of the conversation content.

Microsoft References

Azure AI Speech SDK – Speech to text (SpeechRecognizer) overview and quickstarts.

Azure AI Speech SDK – Text to speech (SpeechSynthesizer) overview and quickstarts.

Azure AI Speech SDK concepts – recognition vs. synthesis pipelines.

To validate that your Azure AI Content Safety resource can identify images containing potential violence, you must call the Image Analyze operation of Content Safety. The REST endpoint for this operation is:

POST {endpoint}/contentsafety/image:analyze?api-version=2024-09-01

This operation analyzes an image for the four harm categories Hate, SelfHarm, Sexual, and Violence. Therefore, the correct base path is /contentsafety, and the correct operation is image analyze (shown in the hotspot as /imageanalyze).

Microsoft References

REST API: Analyze Image — POST {endpoint}/contentsafety/image:analyze?api-version=2024-09-01; categories include Hate, SelfHarm, Sexual, and Violence. Microsoft Learn

Quickstart: Analyze image content with Azure AI Content Safety (overview and examples). Microsoft Learn

Harm categories concept (lists Violence among supported categories). Microsoft Learn

With Azure OpenAI, you don’t call a raw “model name” directly; you call a deployment of that model. SDKs and REST require:

the resource endpoint,

the API key (or Entra ID token), and

the deployment name to target the specific model instance.

References

Azure OpenAI quickstart (connect using endpoint, key, and deployment name).https://learn.microsoft.com/azure/ai-services/openai/quickstart

Chat/completions—path uses /deployments/{deployment-id}.https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions

The HTTP request provided is:

POST https://management.azure.com/subscriptions/18c51a87-3a69-47a8-aedc-a54745f708a1/resourceGroups/RG1/providers/Microsoft.CognitiveServices/accounts/contosol/regenerateKey?api-version=2017-04-18

Body {"keyName": "Key2"}

The request is made against the Azure Cognitive Services Management API (Microsoft.CognitiveServices/accounts/.../regenerateKey).

The regenerateKey operation is specifically designed to regenerate one of the two subscription keys used to authenticate Cognitive Services API calls.

Cognitive Services resources always have two keys: Key1 (primary) and Key2 (secondary).

This design allows key rotation without downtime: you can regenerate one key while using the other in production.

Key Observations:Body Payload:{"keyName": "Key2"}

This explicitly tells Azure to regenerate Key2 (the secondary subscription key).

After the call, the secondary subscription key value changes, while Key1 (primary) remains unaffected.

A. A key for Azure Cognitive Services was generated in Azure Key Vault

Incorrect. The operation is not integrated with Key Vault; it only regenerates Cognitive Services subscription keys.

B. A new query key was generated

Incorrect. Query keys are related to Azure Cognitive Search (not general Cognitive Services).

The request clearly targets CognitiveServices/accounts.

C. The primary subscription key and the secondary subscription key were rotated

Incorrect. The request regenerates only the specified key, not both.

D. The secondary subscription key was reset

Correct. The payload specifies "Key2", so only the secondary subscription key is regenerated.

Option Analysis

Correct Answer: D. The secondary subscription key was reset

Azure REST API – Cognitive Services regenerate key

Authenticate requests to Azure AI services with keys and endpoint

Manage Cognitive Services keys (key1/key2)

Microsoft References

Box 1: api-nam.cognitive.microsofttranslator.com

https://docs.microsoft.com/en-us/azure/cognitive-services/translator/referenc e/v3-0-reference

Box 2: /translate