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| Section | Weight | Objectives |
|---|---|---|
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service |
| Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Build conversational AI and chatbots |
| Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Select suitable AI models - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions |
| 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 |
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NEW QUESTION # 16
You are developing a text processing solution.
You have the function shown below.
For the second argument, you call the function and specify the following string.
Our tour of Paris included a visit to the Eiffel Tower.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 17
You are building a chatbot by using the Microsoft Bot Framework Composer as shown in the exhibit. (Click the Exhibit tab.)
The chatbot contains a dialog named GetUserDetails. GetUserDetails contains a TextInput control that prompts users for their name.
The user input will be stored in a property named name.
You need to ensure that you can dispose of the property when the last active dialog ends.
Which scope should you assign to name?
Answer: B
Explanation:
The dialog scope associates properties with the active dialog. Properties in the dialog scope are retained until the dialog ends.
Incorrect Answers:
A: The conversation scope associates properties with the current conversation. Properties in the conversation scope have a lifetime of the conversation itself. These properties are in scope while the bot is processing an activity associated with the conversation (for example, multiple users together in a Microsoft Teams channel).
B: The user scope associates properties with the current user. Properties in the user scope do not expire. These properties are in scope while the bot is processing an activity associated with the user.
C: The turn scope associates properties with the current turn. Properties in the turn scope expire at the end of the turn.
Reference:
https://docs.microsoft.com/en-us/composer/concept-memory?tabs=v2x
NEW QUESTION # 18
You have an Azure subscription that contains an Azure Al service resource named CSAccount1 and a virtual network named VNet1 CSAaccount1 is connected to VNet1 You need to ensure that only specific resources can access CSAccount1. The solution must meet the following requirements:
* Prevent external access to CSAccount1
* Minimize administrative effort
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.
Answer: A,D
NEW QUESTION # 19
You are developing a photo application that will find photos of a person based on a sample image by using the Face API.
You need to create a POST request to find the photos.
How should you complete the request? 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: findsimilars
https://docs.microsoft.com/en-us/rest/api/faceapi/face/find-similar
Box 2: matchPerson
Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds.
"matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/detectwithurl
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
NEW QUESTION # 20
Your company uses an Azure Cognitive Services solution to detect faces in uploaded images. The method to detect the faces uses the following code.
You discover that the solution frequently fails to detect faces in blurred images and in images that contain sideways faces.
Answer: C
Explanation:
Evaluate different models.
The best way to compare the performances of the detection models is to use them on a sample dataset. We recommend calling the Face - Detect API on a variety of images, especially images of many faces or of faces that are difficult to see, using each detection model. Pay attention to the number of faces that each model returns.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-detection-model
NEW QUESTION # 21
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