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| Section | Weight | Objectives |
|---|---|---|
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
| Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
| Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Integrate vision capabilities into applications - Extract text and handwriting from images - Process and index video content - Analyze images and detect objects/features |
| Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select suitable AI models |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning |
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NEW QUESTION # 145
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 develop an application to identify species of flowers by training a Custom Vision model. You receive images of new flower species.
You need to add the new images to the classifier.
Solution: You add the new images, and then use the Smart Labeler tool.
Does this meet the goal?
Answer: B
Explanation:
The model need to be extended and retrained.
Note: Smart Labeler to generate suggested tags for images. This lets you label a large number of images more quickly when training a Custom Vision model.
NEW QUESTION # 146
You are developing a webpage that will use the Video Indexer service to display videos of internal company meetings.
You embed the Player widget and the Cognitive Insights widget into the page.
You need to configure the widgets to meet the following requirements:
Ensure that users can search for keywords.
Display the names and faces of people in the video.
Show captions in the video in English (United States).
How should you complete the URL for each widget? 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:
Cognitive Insights Widget URL
https://www.videoindexer.ai/embed/insights/{accountId}/{videoId}/?widgets=people, keywords&controls=search Player Widget URL
https://www.videoindexer.ai/embed/player/{accountId}/{videoId}/?showcaptions=true&captions=en-US Comprehensive Detailed ExplanationThe requirements are:
* Ensure that users can search for keywords.
* In the Cognitive Insights Widget, the controls parameter can be set to search to allow keyword search.
* Display the names and faces of people in the video.
* The widgets parameter in the Cognitive Insights Widget determines which insights are shown.
* To show people and keywords, use people,keywords.
* Show captions in the video in English (United States).
* For the Player Widget, captions are controlled by two parameters:
* showcaptions=true enables captions.
* captions=en-US specifies the language.
* Cognitive Insights Widget
* widgets=people,keywords
* controls=search
* Player Widget
* showcaptions=true
* captions=en-US
Final Placement of Values
* Embed Video Indexer widgets
* Video Indexer parameters and customization
Microsoft References
NEW QUESTION # 147
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 148
You are examining the Text Analytics output of an application.
The text analyzed is: "Our tour guide took us up the Space Needle during our trip to Seattle last week." The response contains the data shown in the following table.
Which Text Analytics API is used to analyze the text?
Answer: C
Explanation:
https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/named-entity-recognition/overview Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.
NEW QUESTION # 149
You have a chatbot that uses a QnA Maker application.
You enable active learning for the knowledge base used by the QnA Maker application.
You need to integrate user input into the model.
Which four 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
Graphical user interface, application Description automatically generated
Step 1: For the knowledge base, select Show active learning suggestions.
In order to see the suggested questions, on the Edit knowledge base page, select View Options, then select Show active learning suggestions.
Step 2: Approve and reject suggestions.
Each QnA pair suggests the new question alternatives with a check mark, , to accept the question or an x to reject the suggestions. Select the check mark to add the question.
Step 3: Save and train the knowledge base.
Select Save and Train to save the changes to the knowledge base.
Step 4: Publish the knowledge base.
Select Publish to allow the changes to be available from the GenerateAnswer API.
When 5 or more similar queries are clustered, every 30 minutes, QnA Maker suggests the alternate questions for you to accept or reject.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-base
NEW QUESTION # 150
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