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Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a chatbot that uses question answering in Azure Cognitive Service for Language Users report that the responses of the chatbot lark formality when answering spurious questions You need to ensure that the chatbot provides formal responses to spurious questions. Solution: From Language Studio, you change the chitchat source to qna_chitchit_friindly.tsv. and then retrain and republish the model. Does this meet the goal?
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Question 108 - AI-102 discussion

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Your company uses an Azure Cognitive Services solution to detect faces in uploaded images. The method to detect the faces uses the following code.

You discover that the solution frequently fails to detect faces in blurred images and in images that contain sideways faces. You need to increase the likelihood that the solution can detect faces in blurred images and images that contain sideways faces.

What should you do?

A.
Use a different version of the Face API.
Answers
A.
Use a different version of the Face API.
B.
Use the Computer Vision service instead of the Face service.
Answers
B.
Use the Computer Vision service instead of the Face service.
C.
Use the Identify method instead of the Detect method.
Answers
C.
Use the Identify method instead of the Detect method.
D.
Change the detection model.
Answers
D.
Change the detection model.
Suggested answer: D

Explanation:

Evaluate different models.

The best way to compare the performances of the detection models is to use them on a sample dataset. We recommend calling the Face - Detect API on a variety of images, especially images of many faces or of faces that are difficult to see, using each detection model. Pay attention to the number of faces that each model returns.

The different face detection models are optimized for different tasks. See the following table for an overview of the differences.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-detection-model

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asked 26/09/2024
Kristian Michael Matias
44 questions
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