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The AI-102 Certification Exam is intended for professionals who have experience in designing and implementing AI solutions on the Microsoft Azure platform. Candidates should have a solid understanding of the Azure platform and be familiar with the tools and technologies used to develop and deploy AI solutions. They should also have experience in working with data and be familiar with data science concepts.
Microsoft AI-102 exam is designed for professionals who want to validate their skills and knowledge in designing and implementing AI solutions on the Microsoft Azure platform. Designing and Implementing a Microsoft Azure AI Solution certification is intended for individuals who have experience in programming languages such as Python, C#, and JavaScript, and are familiar with Azure services such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service.
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Microsoft AI-102 is a certification exam that validates the skills and knowledge of individuals in designing and implementing Azure AI solutions. Designing and Implementing a Microsoft Azure AI Solution certification is intended for professionals who have experience in building AI applications and are looking to advance their career in the field of Artificial Intelligence. AI-102 Exam is designed to test the candidate's ability to design and implement AI solutions using Microsoft Azure services.
NEW QUESTION # 262
You have an app that manages feedback.
You need to ensure that the app can detect negative comments by using the Sentiment Analysis API in Azure Cognitive Service for Language. The solution must ensure that the managed feedback remains on your company's internal network.
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.
NOTE: More than one order of answer choices is correct You will receive credit for any of the correct orders you select.
Answer:
Explanation:
* Provision the Language service resource in Azure.
* Deploy a Docker container to an on-premises server.
* Run the container and query the prediction endpoint.
* According to the Microsoft documentation, the Language service is a cloud-based service that provides various natural language processing features, such as sentiment analysis, key phrase extraction, named entity recognition, etc. You can provision the Language service resource in Azure by following the steps in Create a Language resource. You will need to provide a name, a subscription, a resource group, a region, and a pricing tier for your resource. You will also get a key and an endpoint for your resource, which you will use to authenticate your requests to the Language service API.
* According to the Microsoft documentation, you can also use the Language service as a container on your own premises or in another cloud. This option gives you more control over your data and network, and allows you to use the Language service without an internet connection. You can deploy a Docker container to an on-premises server by following the steps in Deploy Language containers. You will need to have Docker installed on your server, pull the container image from the Microsoft Container Registry, and run the container with the appropriate parameters. You will also need to activate your container with your key and endpoint from your Azure resource.
* According to the Microsoft documentation, once you have deployed and activated your container, you
* can run it and query the prediction endpoint to get sentiment analysis results. The prediction endpoint is a local URL that follows this format: http://<container IP address>:<port>/text/analytics/v3.1-preview.4/sentiment. You can send HTTP POST requests to this endpoint with your text input in JSON format, and receive JSON responses with sentiment labels and scores for each document and sentence in your input.
NEW QUESTION # 263
Drag and Drop Question
Drag and Drop Question
You train an Azure AI Custom Vision image classification model used in a mobile app.
You receive 1,000 new images that do not have any associated data.
You need to use the images to retrain the model. You must minimize the amount of time and effort required.
Which three actions should you perform in the Azure AI Custom Vision portal? 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:
Reason being that using the tools (suggested tags) would still apply to the new 1000 images item, even if those 1000 images doesn't associate with the original data pool. So, that means tagging even 1 less image using the suggested tags would still be faster than manually tagging them.
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/suggested-tags
NEW QUESTION # 264
You have an Azure subscription that contains an Azure OpenAI resource named AH.
You build a chatbot that will use AI1 to provide generative answers to specific questions.
You need to ensure that the responses are more creative and less deterministic.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 265
You are building a transcription service for technical podcasts.
Testing reveals that the service fails to transcribe technical terms accurately.
You need to improve the accuracy of the service.
Which five 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/custom-speech-overview#how-does- it-work With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.
- Create a project and choose a model. Use a Speech resource that you create in the Azure portal. If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data.
- Upload test data. Upload test data to evaluate the speech to text offering for your applications, tools, and products.
- Train a model. Provide written transcripts and related text, along with the corresponding audio data. Testing a model before and after training is optional but recommended.
- Deploy a model. Once you ' re satisfied with the test results, deploy the model to a custom endpoint. With the exception of batch transcription, you must deploy a custom endpoint to use a Custom Speech model.
NEW QUESTION # 266
For each of the following statements, select Yes if the statement is tine. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 267
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P.S. Free 2026 Microsoft AI-102 dumps are available on Google Drive shared by ExamDumpsVCE: https://drive.google.com/open?id=1eIBpj21claI0eAq5jVM1JvuE7yyixlvf