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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Computer Vision Solutions | 15-20% | - Implement object detection and image classification - Process video and generate insights - Read and process documents using Azure AI Document Intelligence - Implement Azure AI Face service - Analyze images using Azure AI Vision |
| Topic 2: Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech - Implement speech capabilities using Azure AI Speech - Build question answering solutions |
| Topic 3: Implement Decision Support Solutions | 10-15% | - Integrate Azure AI services into applications - Design and implement decision automation - Monitor and optimize decision support solutions |
| Topic 4: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Create custom skills for Azure AI Search - Implement semantic and vector search - Design document processing pipelines |
| Topic 5: Implement Generative AI Solutions | 10-15% | - Implement prompt engineering techniques - Use Azure OpenAI Service for generative AI - Apply responsible AI practices - Integrate Azure OpenAI models into applications |
| Topic 6: Plan and Manage an Azure AI Solution | 15-20% | - Select appropriate Azure AI services and technologies - Plan and configure secure AI solutions - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions |
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NEW QUESTION # 262
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?
Answer: C
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 new face for 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 LinkedIn 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.microsoft.com/en-us/azure/media-services/video-indexer/customize-person-model- with-api
NEW QUESTION # 263
Drag and Drop Question
You develop a Python app named App1 that performs speech-to-speech translation.
You need to configure App1 to translate English to German.
How should you complete the SpeechTranslationConfig object? 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.
Answer:
Explanation:
Explanation:
Box 1: speechRecognitionLanguage
speechRecognitionLanguage
Gets/Sets the speech recognition language.
Box 2: speechSynthesisLanguage
speechSynthesisLanguage
Gets the language of the speech synthesizer.
Reference:
https://learn.microsoft.com/en-us/javascript/api/microsoft-cognitiveservices-speech- sdk/speechtranslationconfig
NEW QUESTION # 264
You need to measure the public perception of your brand on social media messages. Which Azure Cognitive Services service should you use?
Answer: A
Explanation:
Explanation
Text Analytics Cognitive Service could be used to quickly determine the public perception for a specific topic, event or brand.
Example: A NodeJS app which pulls Tweets from Twitter using the Twitter API based on a specified search term. Then pass these onto Text Analytics for sentiment scoring before storing the data and building a visualisation in PowerBI. The Architecture looked something like this:
Reference:
https://www.linkedin.com/pulse/measuring-public-perception-azure-cognitive-services-steve-dalai
NEW QUESTION # 265
You have an Azure subscription that contains an Azure Al Content Safety resource named CS1. You plan to build an app that will analyze user-gene rated documents and identify obscure offensive terms. You need to create a dictionary that will contain the offensive terms. The solution must minimize development effort.
What should you use?
Answer: B
Explanation:
Azure AI Content Safety supports custom blocklists-dictionaries of terms/phrases you define to detect obscure or domain-specific offensive terms in user content. Creating a blocklist is the minimal-effort, built- in way to meet this requirement; you don't need to build a classifier.
References
* Create and use blocklists in Azure AI Content Safety.https://learn.microsoft.com/azure/ai-services
/content-safety/concepts/blocklists
* Text moderation overview (using built-in classifiers with optional blocklists).https://learn.microsoft.com
/azure/ai-services/content-safety/concepts/text-moderation
NEW QUESTION # 266
You train a Custom Vision 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. The solution must minimize how long it takes to retrain the model.
Which three actions should you perform in the 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:
1.) upload all the images
2.) Get suggested tags
3.) Review the suggestions and confirm the tags
reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/suggested-tags
NEW QUESTION # 267
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