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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles |
| Topic 2: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback |
| Topic 3: Implement computer vision solutions | 10-15% | - Process and index video content - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Build knowledge bases and search indexes |
| Topic 5: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models |
| Topic 6: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Understand agent use cases and types |
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NEW QUESTION # 30
You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.
How should you complete the body of the HTTP 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: LargeFaceListID
LargeFaceList: Add a face to a specified large face list, up to 1,000,000 faces.
Note: Given query face's faceId, to search the similar-looking faces from a faceId array, a face list or a large face list. A "faceListId" is created by FaceList - Create containing persistedFaceIds that will not expire. And a
"largeFaceListId" is created by LargeFaceList - Create containing persistedFaceIds that will also not expire.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
NEW QUESTION # 31
SIMULATION
Use the following login credentials as needed:
- To enter your username, place your cursor in the Sign in box and click on the username below.
- To enter your password, place your cursor in the Enter password box and click on the password below.
Azure Username: admin@abc.com
Azure Password: XXXXXXXXXXXX
The following information is for technical support purposes only:
- Lab Instance: 12345678
Task
You need to build an API that uses the service in Azure AI Services named AAA12345678 to identify whether an image includes a Microsoft Surface Pro or Surface Studio.
To achieve this goal, you must use the sample images in the C:\Resources\Images folder.
To complete this task, sign in to the Azure portal.
Answer:
Explanation:
Step 1: In the Azure dashboard, click Create a resource.
Step 2: In the search bar, type "Cognitive Services."
You'll get information about the cognitive services resource and a legal notice. Click Create.
Step 3: You'll need to specify the following details about the cognitive service (refer to the image below for a completed example of this page):
Subscription: choose your paid or trial subscription, depending on how you created your Azure account.
Resource group: click create new to create a new resource group or choose an existing one.
Region: choose the Azure region for your cognitive service. Choose: East US Azure region.
Name: choose a name for your cognitive service. Enter: AAA12345678
Pricing Tier: Select: Free pricing tier
Step 4: Review and create the resource, and wait for deployment to complete. Then go to the deployed resource.
Note: The Computer Vision Image Analysis service can extract a wide variety of visual features from your images. For example, it can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Tag visual features
Identify and tag visual features in an image, from a set of thousands of recognizable objects, living things, scenery, and actions. When the tags are ambiguous or not common knowledge, the API response provides hints to clarify the context of the tag. Tagging isn't limited to the main subject, such as a person in the foreground, but also includes the setting (indoor or outdoor), furniture, tools, plants, animals, accessories, gadgets, and so on.
Try out the image tagging features quickly and easily in your browser using Vision Studio.
Reference:
https://docs.microsoft.com/en-us/learn/modules/analyze-images-computer-vision/3-analyze- images
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-image- analysis
NEW QUESTION # 32
You are building a message handling system that will use the Azure Translator in Foundry Tools service.
You need to ensure that incoming messages are translated to English.
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 # 33
You are developing a solution for the Management-Bookkeepers group to meet the document processing requirements. The solution must contain the following components:
A From Recognizer resource
An Azure web app that hosts the Form Recognizer sample labeling tool
The Management-Bookkeepers group needs to create a custom table extractor by using the sample labeling tool.
Which three actions should the Management-Bookkeepers group perform in sequence? To answer, move the appropriate cmdlets from the list of cmdlets to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - Create a new project and load sample documents
2 - Label the sample documents
3 - Train a custom model.
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/label-tool
NEW QUESTION # 34
You have an Azure subscription that contains an Azure Al Search resource named AS1.
You implement a custom skill in AS1 that performs language and sentiment analysis of documents.
You ate evaluating the use of AS1 as part of an enrichment pipeline.
In which order will AS1 index the documents? To answer, move all indexing stages from the list of stages to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 35
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