Microsoft AI-102 Online Access

Comprehensive Detailed Explanation

The scenario:

You are building an Azure WebJob to create knowledge bases from an array of URLs.

You already have a QnAMakerClient object instantiated with the relevant API keys.

You need to know what steps are required to create a knowledge base programmatically.

Step 1 – Create a CreateKbDTO object

CreateKbDTO is the request payload for creating a knowledge base.

It contains the name of the knowledge base and its sources, which may include:

A list of URLs.

A list of QnA pairs ( QnADTO ).

A list of files ( FileDTO ).

In this case, since the WebJob provides URLs, they will be passed inside the CreateKbDTO.Sources property.

Step 2 – Call client.Knowledgebase.CreateAsync

Once the CreateKbDTO object is defined, you use the QnAMakerClient to send the request:

var response = await client.Knowledgebase.CreateAsync(createKbDTO);

This triggers the asynchronous creation of a new knowledge base in the QnA Maker service.

Why not A or C?

A. Create a list of FileDTO objects: Not required here, because the question specifies URLs, not files.

C. Create a list of QnADTO objects: Also not needed, since you’re not directly supplying QnA pairs; the knowledge base will be built from URLs.

Correct Answer

B. Call the client.Knowledgebase.CreateAsync method.

D. Create a CreateKbDTO object.

Microsoft References

QnA Maker – Create Knowledge Base API

QnAMakerClient Class – Azure SDK for .NET

CreateKbDTO Class

The question is about creating an Azure Cognitive Services resource programmatically with C#.

Key requirements from the question:

Free resource → This requires the F0 (Free) pricing tier.

West US region → Location should be " westus " .

Generate captions of images automatically → This requires the Computer Vision service.

Option Analysis

A. create_resource(client, " res1 " , " ComputerVision " , " F0 " , " westus " )

Uses " ComputerVision " kind → Correct, because Computer Vision provides the image captioning feature.

Tier is " F0 " → Correct, free tier.

Location " westus " → Correct.

This is the correct answer.

B. create_resource(client, " res1 " , " CustomVision.Prediction " , " F0 " , " westus " )

Custom Vision is for building and deploying custom image classifiers.

The Prediction resource is specifically for running inference, not general captioning.

Wrong service for the requirement.

C. create_resource(client, " res1 " , " ComputerVision " , " S0 " , " westus " )

" ComputerVision " is correct.

But " S0 " is a paid tier, not the required free tier.

Does not meet the requirement.

D. create_resource(client, " res1 " , " CustomVision.Prediction " , " S0 " , " westus " )

Wrong service (captioning is not handled by Custom Vision).

Wrong tier (paid instead of free).

Incorrect.

Correct Answer: A. create_resource(client, " res1 " , " ComputerVision " , " F0 " , " westus " )

Microsoft References

Azure AI Computer Vision documentation

Create a Cognitive Services resource in Azure

Computer Vision capabilities – Image analysis and captioning

A view is a virtual table based on the result set of a query.

Views allow you to encapsulate complex queries (including joins and calculated columns) and make them reusable by other users.

Index improves performance but does not store or rerun queries.

Scalar function returns a single value, not a tabular result.

Table stores raw data, not queries.

An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, Address could have the following structure:

Address: 4567 Main Street, NY, 98052, USA

Building Number: 4567

Street Name: Main Street

State: NY

Zip Code: 98052

Country: USA