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Microsoft AI-102 exam is a valuable certification for professionals who want to demonstrate their expertise in designing and implementing AI solutions on the Microsoft Azure platform. It covers a wide range of topics related to machine learning, data preprocessing, model deployment, and monitoring. Candidates can prepare for AI-102 exam by taking advantage of various resources and certification programs offered by Microsoft.
Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution Exam is a certification exam designed to test your knowledge of designing and implementing AI solutions using Microsoft Azure technologies. AI-102 Exam is intended for the individuals who have a strong understanding of Microsoft Azure AI services and want to demonstrate their expertise in designing and implementing AI solutions.
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Microsoft AI-102 exam measures your ability to build and deploy AI models, integrate AI services into applications, and monitor and optimize AI solutions. AI-102 exam focuses on three key areas: designing AI solutions, implementing AI solutions, and monitoring and optimizing AI solutions. AI-102 Exam also covers various Azure AI services, including Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service.
NEW QUESTION # 66
You are developing an internet-based training solution for remote learners.
Your company identifies that during the training, some learners leave their desk for long periods or become distracted.
You need to use a video and audio feed from each learner's computer to detect whether the learner is present and paying attention. The solution must minimize development effort and identify each learner.
Which Azure Cognitive Services service should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Graphical user interface, text, application, email Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/what-are-cognitive-services
NEW QUESTION # 67
You are developing an internet-based training solution for remote learners.
Your company identifies that during the training, some learners leave their desk for long periods or become distracted.
You need to use a video and audio feed from each learner's computer to detect whether the learner is present and paying attention. The solution must minimize development effort and identify each learner.
Which Azure Cognitive Services service should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/what-are-cognitive-services
NEW QUESTION # 68
You have an Azure subscription that contains an Azure OpenAl resource named All and ari Azure Al Content Safety resource named CS1.
You build a chatbot that uses All to provide generative answers to specific questions and CS1 to check input and output for objectionable content You need to optimize the content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Moderate text content feature to run the tests.
Does this meet the requirement?
Answer: A
Explanation:
Comprehensive Detailed Explanation
You have a chatbot that uses Azure OpenAI for generative responses and Azure AI Content Safety to filter objectionable content.
The requirement is to optimize the content filter configurations by running tests on sample questions.
The proposed solution: Using Content Safety Studio # Moderate text content feature.
This solution is correct because:
Content Safety Studio provides tools to test and fine-tune content filters.
The Moderate text content feature allows you to run sample prompts and responses against filters for categories such as sexual, violence, hate, and self-harm.
This is exactly how you optimize and validate configurations before applying them to production workloads.
The Answer: Yes
Microsoft Reference:
Content Safety Studio overview
Azure AI Content Safety
NEW QUESTION # 69
You have a Language Understanding resource named lu1.
You build and deploy an Azure bot named bot1 that uses lu1.
You need to ensure that bot1 adheres to the Microsoft responsible AI principle of inclusiveness.
How should you extend bot1?
Answer: A
Explanation:
Inclusiveness: AI systems should empower everyone and engage people.
Direct Line Speech is a robust, end-to-end solution for creating a flexible, extensible voice assistant. It is powered by the Bot Framework and its Direct Line Speech channel, that is optimized for voice-in, voice-out interaction with bots.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/direct-line-speech
NEW QUESTION # 70
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.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation
You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:
Create a project
Before anything else, you must create a Custom Vision project in the Azure AI portal.
This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).
Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.
Upload and tag images
You must provide training data.
Upload a set of images showing both faulty and non-faulty components.
Tag each image correctly so that the model can learn from labeled examples.
Train the classifier model
Once images are tagged, you run training on the dataset to generate the classifier model.
The model can then be published and used for inference via API calls.
Why not the other options?
Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.
Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.
Correct Sequence:
Create a project
Upload and tag images
Train the classifier model
Microsoft References
Quickstart: Build an image classification project with the Custom Vision portal Custom Vision overview Train and test a Custom Vision model
NEW QUESTION # 71
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