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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Computer Vision Solutions | 15-20% | - Analyze images using Azure AI Vision - Implement object detection and image classification - Read and process documents using Azure AI Document Intelligence - Implement Azure AI Face service - Process video and generate insights |
| Topic 2: Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Manage AI costs and resource allocation - Select appropriate Azure AI services and technologies - Plan and configure secure AI solutions |
| Topic 3: Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Implement prompt engineering techniques - Apply responsible AI practices - Integrate Azure OpenAI models into applications |
| Topic 4: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Create custom skills for Azure AI Search - Implement Azure AI Search solutions - Implement semantic and vector search - Design document processing pipelines |
| Topic 5: Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Integrate Azure AI services into applications - Design and implement decision automation |
| Topic 6: Implement Natural Language Processing Solutions | 20-25% | - Translate text and speech - Analyze text using Azure AI Language - Create conversational AI solutions with Azure AI Bot Service - Implement speech capabilities using Azure AI Speech - Build question answering solutions |
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NEW QUESTION # 232
You are building content for a video training solution.
You need to create narration to accompany the video content. The solution must use Custom Neural Voice.
What should you use to create a custom neural voice, and which service should you use to generate the narration? To answer, select the appropriate options in the answer area.
NOTE: Each correct answer is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 233
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 remove all the chit-chat question and answer pairs, and then retrain and republish the model Does this meet the goal?
Answer: A
Explanation:
Removing all the chit-chat question and answer pairs from the project will not ensure that the chatbot provides formal responses to spurious questions. It will only make the chatbot unable to handle any chit-chat scenarios, which may result in a poor user experience and a loss of engagement. Instead, you should choose a chit-chat personality that matches the tone and style of your chatbot, such as Professional or Caring. You can also edit the chit-chat questions and answers to suit your specific needs, or add new ones that are not in the predefined data set12. This way, you can ensure that the chatbot responds appropriately to spurious questions, while still maintaining a conversational and engaging interaction with the user.
NEW QUESTION # 234
Hotspot Question
You plan to deploy a containerized version of an Azure AI Language service that will be used for sentiment analysis.
You configure https://contoso.cognitiveservices.azure.comas the endpoint URI for the service, and you pull the latest version of the Language service Sentiment Analysis container.
You need to run the container on an Azure virtual machine by using Docker.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment To run the Sentiment Analysis v3 container, execute the following docker run command.
docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \ Eula=accept \ Billing={ENDPOINT_URI} \ ApiKey={API_KEY} is the endpoint for accessing the Text Analytics API. https://<your-custom- subdomain>.cognitiveservices.azure.com Box 2: https://contoso.cognitiveservices.azure.com
{ENDPOINT_URI} is the endpoint for accessing the Text Analytics API: https://<your-custom- subdomain>.cognitiveservices.a The endpoint for accessing the Text Analytics API. zure.com Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics- how-to-install-containers?tabs=sentiment
https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/sentiment-opinion- mining/how-to/use-containers#run-the-container-with-docker-run
NEW QUESTION # 235
You need to develop code to upload images for the product creation project. The solution must meet the accessibility requirements.
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:
Reference:
https://github.com/Azure-Samples/cognitive-services-dotnet-sdk-samples/blob/master/documentation-samples/quickstarts/ComputerVision/Program.cs
NEW QUESTION # 236
You have an Azure subscription that contains an Azure Al Document Intelligence resource named DM.
You build an app named App1 that analyzes PDF files for handwritten content by using DM.
You need to ensure that App1 will recognize the handwritten content.
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:
Comprehensive Detailed Explanation
The requirement:
You have an Azure AI Document Intelligence resource (formerly Form Recognizer).
You want the app to detect handwritten content inside PDF files.
Step 1 - Choosing the correct model
prebuilt-document: Extracts structure and text from documents, but not optimized specifically for handwriting.
prebuilt-contract: Designed for contracts, not general handwriting.
prebuilt-read: Extracts text (printed and handwritten) from documents. Supports handwritten content recognition.
Therefore, to analyze handwritten content, the correct choice is prebuilt-read.
Step 2 - Confidence threshold
The style.Confidence value indicates the confidence that the text is handwritten.
A typical threshold used is 0.75 (75%).
This ensures that only results with reasonably high confidence are considered handwritten.
Values like 0.1 would be too low (allowing false positives), while 1.0 might miss valid handwriting because it requires absolute certainty.
The Answer:
Model: prebuilt-read
Confidence threshold: 0.75
NEW QUESTION # 237
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