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

For a table projection in a knowledge store (skillset definition), the table node must define:

tableName : the table to project into (physical expression).

generatedKeyName : the name of the system-generated key column.

source : an object-shaping expression (projection path) describing what document content flows into the table.

dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.

References

Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.

The requirement: Ensure the chatbot blocks questions that attempt to circumvent built-in safety features .

This is known as a jailbreak attempt (e.g., prompt injection, role-play tricking).

Azure AI Content Safety recently added Jailbreak risk detection for precisely this purpose.

Other options:

Protected material detection → for copyright/IP detection.

Monitor online activity → not related to OpenAI safety.

Moderate text content → general harmful content moderation (violence, hate, sexual, self-harm), but not jailbreaks.

Microsoft Reference:

Azure AI Content Safety jailbreak risk detection

ï‚· Provide access to AI1 by using: A bearer token

ï‚· Connect to the deployment by using: A deployment name

To ensure only your apps can access the Azure OpenAI resource, authenticate with Microsoft Entra ID (Azure AD) and obtain a Bearer token (ideally via managed identity ). This avoids sharing long-lived API keys and lets you scope access by app identity and Azure RBAC. Microsoft’s guidance recommends token-based auth for Azure OpenAI and shows how to acquire a bearer token/managed identity token for calls

When calling the REST API , Azure OpenAI requires you to specify the deployment name to select which model deployment to use. The REST route includes {deployment-id} (deployment name) in the path, e.g., .../openai/deployments/{deployment-id}/chat/completions. This is explicitly documented as a key difference from OpenAI’s public API, which takes only a model.

Therefore:

Access method: Bearer token (via Entra ID/managed identity)

Deployment selector: Deployment name in the REST path

Microsoft References

Authenticate to Azure OpenAI with Microsoft Entra ID / Managed identity (bearer token): guidance and how-to

Azure OpenAI request header recommends token-based authentication (Bearer).

Azure OpenAI requires deployment name in API calls; REST path uses

For a social media messaging app, you need real-time language identification on short text messages. The Azure AI Translator service provides a dedicated Detect API that returns the language and a confidence score for each input text, designed for low-latency scenarios such as chat and in-app communication. This aligns directly with “identify in real time the language used in messages.” Microsoft Azure +2

Why the other options are not the best fit:

Azure AI Speech (A) targets spoken audio (speech-to-text, translation, diarization). It’s not intended for detecting the language of text messages. Azure AI

Azure AI Content Safety (B) focuses on safety/moderation (harmful or unsafe content), not language identification.

Azure AI Language (D) also offers a Language Detection feature, but it’s typically used within broader text analytics workflows. For real-time messaging and potential downstream translation, Translator’s Detect endpoint is the targeted and streamlined choice.

References (Microsoft Learn)

Translator – Detect method (returns detected language + confidence). Microsoft Learn

Translator – Product overview and real-time translation scenarios. Microsoft Azure

Translator – Language support (Auto Language Detection). Microsoft Learn

Azure AI Language – Language Detection (feature overview/how-to) for context.

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You are building a Text-to-Speech (TTS) solution in Azure Cognitive Services Speech that must:

Convert up to 400 characters (short-form TTS, not long audio synthesis).

Provide multiple voice options to users.

Minimize costs (short-form TTS is cheaper than long-form neural synthesis).

To provide a list of available voices, Azure Speech exposes a REST API endpoint:

Correct endpoint:

https: / / < region > .tts.speech.microsoft.com/cognitiveservices/voices/list

This endpoint returns a list of all available voices (standard + neural) supported in the region, including locale, gender, and style.

Evaluating the Options:

Option A: /longaudiosynthesis/voices

Wrong. This is for long audio synthesis (narration/long documents).

More expensive and not needed for short social media messages.

Option B: /cognitiveservices/voices/list ✅

Correct. This endpoint lists available voices for short-form text-to-speech, which matches the requirements.

Option C: /cognitiveservices/v1?deploymentId={deployment}

Wrong. This is for using a custom voice deployment.

Not required here since the solution should minimize cost and use standard available voices.

Option D: /speechtotext/v3.0/models/base

Wrong. This is for speech-to-text (STT), not text-to-speech.

Correct Answer:

B. https://uksouth.tts.speech.microsoft.com/cognitiveservices/voices/list

Microsoft References

Azure Speech Service REST API reference

List voices in Azure Text-to-Speech

The requirement is to identify the created Azure AI services account within a DevOps pipeline.

The az cognitiveservices account show command returns the details (name, id, location, etc.) of an existing Azure AI service account.

az resource link creates or manages links between resources, not what we need.

az account list lists Azure subscriptions, not resources.

az cognitiveservices account network-rule manages network access rules but does not return account identity.

Microsoft References:

az cognitiveservices account show

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

Scenario Recap:

IoT data from 100 machines × 50 sensors × 1-minute intervals for 1 year = 5,000 time series datasets .

Requirement: Detect unusual values (outliers) to predict failures.

Correct Service:

Azure Anomaly Detector (A) is designed for time-series anomaly detection.

It learns temporal patterns and detects anomalies in sensor/IoT data streams.

Other options:

Cognitive Search = full-text search

Form Recognizer = extract text/fields from documents

Custom Vision = image classification

Answer: A. Anomaly Detector

You are calling the Face API to find similar faces from an existing list called employeefaces.

Key facts from the scenario:

The list contains 60,000 images.

In Face API:

faceListId → limited to 1,000 faces.

largeFaceListId → designed for very large lists, up to 1,000,000 faces.

Since employeefaces has 60,000 images, you must use LargeFaceListId, not faceListId.

Parameters in the Request Body

" faceId " : " 18c51a87-3a69-47a8-aedc-a54745f708a1 "

This is the face to search for.

" largeFaceListId " : " employeefaces "

Must use LargeFaceListId because the list has 60,000 images.

" maxNumOfCandidatesReturned " : 1

Limits the number of matches returned.

" mode " : " matchFace "

Two options exist:

" matchFace " → returns the most similar individual faces.

" matchPerson " → groups faces by person.

Since the requirement is find similar faces, use " matchFace " .

Completed Answer

{

" faceId " : " 18c51a87-3a69-47a8-aedc-a54745f708a1 " ,

" largeFaceListId " : " employeefaces " ,

" maxNumOfCandidatesReturned " : 1 ,

" mode " : " matchFace "

}

Correct Values to Drag and Drop

LargeFaceListId → " employeefaces "

matchFace → " mode "

Final Answer:

First blank: LargeFaceListId

Second blank: matchFace

Microsoft References

Face API – Find Similar

Face API – Large Face List operations