AI-102: Designing and Implementing a Microsoft Azure AI Solution (beta)

You are developing a webpage that will use the Video Indexer service to display videos of internal company meetings.

You embed the Player widget and the Cognitive Insights widget into the page.

You need to configure the widgets to meet the following requirements:

Ensure that users can search for keywords.

Display the names and faces of people in the video.

Show captions in the video in English (United States).

How should you complete the URL for each widget? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Reference:https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-embed-widgets

Reference:

https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-embed-widgets

You have a Computer Vision resource named contoso1 that is hosted in the West US Azure region.

You need to use contoso1 to make a different size of a product photo by using the smart cropping feature.

How should you complete the API URL? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Reference:https://westus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21bhttps://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-generating-thumbnails#examples

Reference:

https://westus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b

https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-generating-thumbnails#examples

You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.

You need to use the Custom Vision API to help detect common faults.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Step 1: Create a projectCreate a new project.Step 2: Upload and tag the imagesChoose training images. Then upload and tag the images.Step 3: Train the classifier model.Train the classifierReference:https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier

Step 1: Create a project

Create a new project.

Step 2: Upload and tag the images

Choose training images. Then upload and tag the images.

Step 3: Train the classifier model.

Train the classifier

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier

You train a Custom Vision model to identify a company's products by using the Retail domain.

You plan to deploy the model as part of an app for Android phones.

You need to prepare the model for deployment.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-your-model

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-your-model

You are developing an application to recognize employees’ faces by using the Face Recognition API. Images of the faces will be accessible from a URI endpoint.

The application has the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/use-persondirectory

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/use-persondirectory

You have a Custom Vision resource named acvdev in a development environment.

You have a Custom Vision resource named acvprod in a production environment.

In acvdev, you build an object detection model named obj1 in a project named proj1.

You need to move obj1 to acvprod.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/copy-move-projects

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/copy-move-projects

You have a Video Indexer service that is used to provide a search interface over company videos on your company's website. You need to be able to search for videos based on who is present in the video.

What should you do?

Create a person model and associate the model to the videos.
Create person objects and provide face images for each object
Invite the entire staff of the company to Video Indexer.
Edit the faces in the videos.
Upload names to a language model.

Correct answer: A

Explanation:

Video Indexer supports multiple Person models per account. Once a model is created, you can use it by providing the model ID of a specific Person model when uploading/indexing or reindexing a video. Training a newfacefora video updates the specific custom model that the video was associated with.Note: Video Indexer supports face detection and celebrity recognition for video content. The celebrity recognition feature covers about one million faces based on commonly requested data source such as IMDB, Wikipedia,and top Linkedln influencers. Faces that aren't recognized by the celebrity recognition feature are detected but left unnamed. Once you label a face with a name, the face and name get added to your account's Person model.Video Indexer will then recognize this face in your future videos and past videos.Reference:https://docs. mi crosoft. com/en-us/azu re/med ia -servi ces/vi deo-i ndexer/customize-pers on-mo del-with-api

Video Indexer supports multiple Person models per account. Once a model is created, you can use it by providing the model ID of a specific Person model when uploading/indexing or reindexing a video. Training a newfacefor

a video updates the specific custom model that the video was associated with.

Note: Video Indexer supports face detection and celebrity recognition for video content. The celebrity recognition feature covers about one million faces based on commonly requested data source such as IMDB, Wikipedia,

and top Linkedln influencers. Faces that aren't recognized by the celebrity recognition feature are detected but left unnamed. Once you label a face with a name, the face and name get added to your account's Person model.

Video Indexer will then recognize this face in your future videos and past videos.

Reference:

https://docs. mi crosoft. com/en-us/azu re/med ia -servi ces/vi deo-i ndexer/customize-pers on-mo del-with-api

You use the Custom Vision service to build a classifier.

After training is complete, you need to evaluate the classifier.

Which two metrics are available for review? Each correct answer presents a complete solution. (Choose two.) NOTE: Each correct selection is worth one point.

recall
F-score
weighted accuracy
precision
area under the curve (AUC)

Correct answer: AD

Explanation:

Custom Vision provides three metrics regarding the performance of your model: precision, recall, and AP.Reference:https://www.tallan.com/blog/2020/05/19/azure-custom-vision/

Custom Vision provides three metrics regarding the performance of your model: precision, recall, and AP.

Reference:

https://www.tallan.com/blog/2020/05/19/azure-custom-vision/

You are developing a method that uses the Computer Vision client library. The method will perform optical character recognition (OCR) in images. The method has the following code.

During testing, you discover that the call to the GetReadResultAsync method occurs before the read operation is complete.

You need to prevent the GetReadResultAsync method from proceeding until the read operation is complete.

Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.)

NOTE: Each correct selection is worth one point.

Remove the Guid.Parse (operationid) parameter.
Add code to verify the results.Status value.
Add code to verify the status of the txtHeaders. status value.
Wrap the call to GetReadResultAsync within a loop that contains a delay.

Correct answer: BD

Explanation:

Example code :do{results = await client.GetReadResultAsync(Guid.Parse(operationId));}while ((results.Status == OperationStatusCodes.Running ||results.Status == OperationStatusCodes.NotStarted));Reference:https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/dotnet/ComputerVision/ComputerVisionQuickstart.cs

Example code :

do

{

results = await client.GetReadResultAsync(Guid.Parse(operationId));

}

while ((results.Status == OperationStatusCodes.Running ||

results.Status == OperationStatusCodes.NotStarted));

Reference:

https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/dotnet/ComputerVision/ComputerVisionQuickstart.cs

You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.

How should you complete the body of the HTTP request? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Box 1: LargeFaceListID LargeFaceList: Add a face to a specified large face list, up to 1,000,000 faces.Note: Given query face's faceId, to search the similar-looking faces from a faceId array, a face list or a large face list. A "faceListId" is created by FaceList - Create containing persistedFaceIds that will not expire. And a "largeFaceListId" is created by LargeFaceList - Create containing persistedFaceIds that will also not expire.Incorrect Answers:Not "faceListId": Add a face to a specified face list, up to 1,000 faces.Box 2: matchFaceFind similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.Reference:https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar

Box 1: LargeFaceListID

LargeFaceList: Add a face to a specified large face list, up to 1,000,000 faces.

Note: Given query face's faceId, to search the similar-looking faces from a faceId array, a face list or a large face list. A "faceListId" is created by FaceList - Create containing persistedFaceIds that will not expire. And a "largeFaceListId" is created by LargeFaceList - Create containing persistedFaceIds that will also not expire.

Incorrect Answers:

Not "faceListId": Add a face to a specified face list, up to 1,000 faces.

Box 2: matchFace

Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.

Reference:

https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar

You are developing a photo application that will find photos of a person based on a sample image by using the Face API.

You need to create a POST request to find the photos.

How should you complete the request? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Box 1: detectFace - Detect With Url: Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes.POST {Endpoint}/face/v1.0/detectBox 2: matchPersonFind similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.Reference:https://docs.microsoft.com/en-us/rest/api/faceapi/face/detectwithurlhttps://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar

Box 1: detect

Face - Detect With Url: Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes.

POST {Endpoint}/face/v1.0/detect

Box 2: matchPerson

Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.

Reference:

https://docs.microsoft.com/en-us/rest/api/faceapi/face/detectwithurl

https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar

Vendor:	Microsoft
Exam Code:	AI-102
Exam Name:	Designing and Implementing a Microsoft Azure AI Solution (beta)
Date:	Mar 08, 2025
File Size:	11 MB
Downloads:	7

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