AI-102 Practice Questions

Typing: In a .chat transcript, you can simulate the bot’s typing indicator with the Typing activity, optionally followed by a [Delay=...]. This matches the intent to pause before sending the next message.

AttachmentLayout=carousel: When sending multiple attachments (the two watch images), set the layout to carousel so the Emulator displays the attachments horizontally as a card carousel.

Adaptive Card attachment: The JSON file cards\watchProfileCard.json represents an Adaptive Card; specify adaptivecard so the Emulator renders the card.

Comprehensive Detailed Explanation along with All References available from Microsoft Azure AI Solution at the end of the explanationResulting .chat snippet (essentials):

user=User1

bot=watchbot

user: I want a new watch.

bot: [Typing][Delay=3000]

bot: I can help you with that! Let me see what I can find.

bot: Here's what I found.

bot:

[AttachmentLayout=carousel]

[Attachment=https://contoso.blob.core.windows.net/watch01.jpg]

[Attachment=https://contoso.blob.co re.windows.net/watch02.jpg]

user: I like the first one.

bot: Sure, pulling up more information.

bot: [Attachment=cards\watchProfileCard.json adaptivecard]

user: That's nice! Thank you.

bot: Sure, you are most welcome!

Bot Framework Emulator transcript (.chat) activities and typing indicator: Bot Framework Activity schema; Emulator supports typing and delay in transcripts.https://learn.microsoft.com/azure/bot-service/bot-service-debug-emulator https://learn.microsoft.com/azure/bot-service/rest-api/bot-framework-rest-connector-api-reference#activity-object

Attachment layout (carousel / list) for multiple attachments:https://learn.microsoft.com/azure/bot-service/nodejs/bot-builder-nodejs-send-rich-cards#carousel-layout

Adaptive Cards in Bot Framework:https://learn.microsoft.com/adaptive-cards/get-started/bots https://learn.microsoft.com/azure/bot-service/bot-builder-howto-add-media-attachments#adaptive-cards

References

To detect hateful content with Azure AI Foundry Content Safety – Text, you should read the category analysis results returned by the analyze_text call. The response exposes a collection named categories_analysis, where each item contains the analyzed category (e.g., TextCategory.HATE) and its severity level.

Hence:

Iterate over response.categories_analysis, filter the item where item.category == TextCategory.HAT

E.  

Print the severity using hate_result.severity.

This uses category analysis (not blocklist matches), which is the correct mechanism for detecting hate content with a severity score.

Microsoft References

Azure AI Content Safety – Analyze text (Python quickstart/samples): iterate response.categories_analysis, check TextCategory.HATE, read item.severity.

Azure AI Content Safety – Text categories and severity levels: explains categories (Hate, Violence, Sexual, Self-harm) and severity output.

Upload to: An Azure Storage account

Model type: Custom neural

You have 1,000 scanned hand-written survey responses with no consistent layout. The goal is to extract data while minimizing development effort.

Step 1: Upload location

For Azure AI Document Intelligence (formerly Form Recognizer), training data (images, PDFs) must be placed in a container in an Azure Storage account.

Options like Cosmos DB or Azure Files share are not valid training data sources for Document Intelligence.

Therefore, you upload the survey images to Azure Storage account.

Step 2: Model type

The surveys are handwritten and do not have a consistent layout.

Custom template models work only when the layout is fixed and consistent (e.g., invoices with the same fields in the same place).

Identity document (ID) model is a prebuilt model for passports, driver’s licenses, etc., not for surveys.

Custom neural model is designed to handle unstructured or variable-layout forms, including handwritten content. This best fits the survey scenario.

Thus, the correct configuration is:

Upload to Azure Storage account

Use Custom neural model

Correct Answer:

Upload to: An Azure Storage account

Model type: Custom neural

Azure AI Document Intelligence – Custom models

Custom neural vs custom template models

Train a custom model with Azure Storage input

Microsoft References

Comprehensive Detailed ExplanationYou need to analyze confidential documents with the Language service while ensuring those documents never leave your premises. The supported pattern for Azure AI Services (Cognitive Services) is to run the Language service container on-premises and send the app’s requests to that local container endpoint. The container performs inference locally; only metering/billing calls are sent to Azure.

To achieve this:

Provision an on-premises Kubernetes cluster that has internet connectivity.Azure AI service containers running on-prem require outbound access to Azure for metering and billing unless you’re using the special disconnected model (not implied here). Therefore, the cluster must have internet connectivity so the container can reach the Azure billing endpoint while keeping document content local.

Pull an image from the Microsoft Container Registry (MCR).Official Azure AI service containers (including Language) are published in MCR, not Docker Hub. You must pull the Language (Text Analytics/Language) container image from MCR.

Run the container and specify an API key and the Endpoint URL of the Cognitive Services resource.When you start the container, you configure it with the API key and billing (endpoint) URL of your Azure Cognitive Services (multi-service or Language) resource. This enables the container to authenticate and report usage to Azure, while your data stays on-prem.

Why not the other options?

AKS would place workloads in Azure, which isn’t required when the requirement is to keep documents on-prem.

On-prem cluster isolated from the internet would block the container’s required calls to Azure’s billing endpoint.

Pull from Docker Hub is incorrect because Azure AI service containers are published to MCR.

App ID and Client Secret are not the standard parameters for Azure AI service containers; they require API key + billing endpoint.

Use Azure AI services in containers (billing/endpoint and API key requirements, data remains local):Microsoft Learn – Install and run containers for Azure AI services and Use containers with Azure AI services (metering and billing).https://learn.microsoft.com/azure/ai-services/containers/

Language service container images on Microsoft Container Registry (MCR):Microsoft Learn – Deploy language containers (Text Analytics/Language).https://learn.microsoft.com/azure/ai-services/language-service/how-to/use-containers

Networking requirements (internet connectivity for metering):Microsoft Learn – Configure containers and network access for Azure AI services containers.https://learn.microsoft.com/azure/ai-services/containers/container-requirements-and-limits

Microsoft Azure AI Solution ReferencesThese documents collectively show that: (a) containers are pulled from MCR, (b) they are configured with API key + endpoint for billing, and (c) internet egress is required for metering while payload data remains on-premises.

B: The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative", "neutral" and "positive") and confidence scores at the sentence and document-level.

D: The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.

This capability is useful for content stores that collect arbitrary text, where language is unknown. Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- sentiment-analy sis?tabs=version-3-1

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- language-detection

1. Number of Projection GroupsA projection group in a knowledge store is an array of projections (tables, objects, or files) that are stored together.

In the provided JSON, the projections array contains the following two top-level sections:

The array of tables:

JSON

"tables": [

{ ... },

{ ... }

]

The array of objects:

JSON

"objects": [

{ ... }

]

Each of these top-level arrays (tables and objects) represents a distinct projection group in the knowledge store. Therefore, there are two projection groups.

2. Image SavingThe files projection is used to save images, normalized images, or application files generated during the enrichment process.

In the provided JSON, the files array is present but is empty:

JSON

"files": []

Since there are no file projections defined, the images (or any files) generated during the enrichment process will not be saved to the knowledge store. They are processed in the pipeline but not persisted in the output storage defined by the knowledgeStore configuration.

Box 1: "categories": ["Locations", "Persons", "Organizations"],

Locations, Persons, Organizations are in the outputs.

Scenario: Contoso plans to develop a searchable knowledgebase of all the intellectual property

Note: The categories parameter is an array of categories that should be extracted. Possible category types: "Person", "Location", "Organization", "Quantity", "Datetime", "URL", "Email". If no category is provided, all types are returned.

Box 2: {"name": " entities"}

The include wikis, so should include entities in the outputs.

Note: entities is an array of complex types that contains rich information about the entities extracted from text, with the following fields

name (the actual entity name. This represents a "normalized" form)

wikipediaId

wikipediaLanguage

wikipediaUrl (a link to Wikipedia page for the entity)

etc.