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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
| Topic 2: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select suitable AI models - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 3: 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 |
| Topic 4: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
| Topic 5: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Integrate vision capabilities into applications - Build and deploy custom vision models - Analyze images and detect objects/features - Process and index video content |
| Topic 6: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration |
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NEW QUESTION # 65
You are building a chatbot that will provide information to users as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Adaptive card
Box 2: an image
Reference:
https://docs.microsoft.com/en-us/microsoftteams/platform/task-modules-and-cards/cards/cards-reference
https://docs.microsoft.com/en-us/composer/how-to-send-cards?tabs=v1x
NEW QUESTION # 66
You are building a model that will be used in an iOS app.
You have images of cats and dogs. Each image contains either a cat or a dog.
You need to use the Custom Vision service to detect whether the images is of a cat or a dog.
How should you configure the project in the Custom Vision portal? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Classification
Box 2: Multiclass
A multiclass classification project is for classifying images into a set of tags, or target labels. An image can be assigned to one tag only.
Box 3: General
General: Optimized for a broad range of image classification tasks. If none of the other specific domains are appropriate, or if you're unsure of which domain to choose, select one of the General domains.
Reference:
https://cran.r-project.org/web/packages/AzureVision/vignettes/customvision.html
NEW QUESTION # 67
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three 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. (Choose three.)
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 68
You have a product knowledgebase that contains multiple PDF documents.
You need to build a chatbot that will provide responses based on data in the knowledgebase. The solution must minimize development effort and costs.
What should you include in the solution?
Answer: C
Explanation:
* The requirement: A chatbot that answers based on a product knowledgebase (multiple PDFs).
* Best choice: Custom Question Answering in Azure AI Language.
* It allows you to ingest documents like PDFs, FAQs, manuals.
* Minimal development effort since you just upload documents and the service automatically indexes them.
* Other options:
* CLU # for intent/intent-based classification, not for knowledgebases.
* Language detection # only detects language, not Q&A.
* Azure OpenAI # could work, but more costly and requires prompt engineering (not the
"minimize effort/cost" requirement).
Microsoft Reference:
Custom question answering in Azure AI Language
NEW QUESTION # 69
You are developing a text processing solution.
You have the function shown below.
Answer:
Explanation:
NEW QUESTION # 70
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