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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Select suitable AI models - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources |
| Topic 2: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Customize and deploy NLP models - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection |
| Topic 3: 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 4: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data |
| Topic 5: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service |
| Topic 6: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback - Deploy and manage generative models |
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NEW QUESTION # 29
Your company wants to reduce how long it takes for employees to log receipts in expense reports. All the receipts are in English.
You need to extract top-level information from the receipts, such as the vendor and the transaction total. The solution must minimize development effort.
Which Azure Cognitive Services service should you use?
Answer: C
Explanation:
Explanation
Azure Form Recognizer is a cognitive service that lets you build automated data processing software using machine learning technology. Identify and extract text, key/value pairs, selection marks, tables, and structure from your documents-the service outputs structured data that includes the relationships in the original file, bounding boxes, confidence and more.
Form Recognizer is composed of custom document processing models, prebuilt models for invoices, receipts, IDs and business cards, and the layout model.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer
NEW QUESTION # 30
You are developing the shopping on-the-go project.
You need to build the Adaptive Card for the chatbot.
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:
NEW QUESTION # 31
You have data saved in the following format.
Which format was used?
Answer: A
NEW QUESTION # 32
You build a QnA Maker resource to meet the chatbot requirements.
Which RBAC role should you assign to each group? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Cognitive Service User
Ensure that the members of a group named Management-Accountants can approve the FAQs.
Approve=publish.
Cognitive Service User (read/write/publish): API permissions: All access to Cognitive Services resource except for ability to:
1. Add new members to roles.
2. Create new resources.
Box 2: Cognitive Services QnA Maker Editor
Ensure that the members of a group named Consultant-Accountants can create and amend the FAQs.
QnA Maker Editor: API permissions:
1. Create KB API
2. Update KB API
3. Replace KB API
4. Replace Alterations
5. "Train API" [in new service model v5]
Box 3: Cognitive Services QnA Maker Read
Ensure that the members of a group named the Agent-CustomerServices can browse the FAQs.
QnA Maker Read: API Permissions:
1. Download KB API
2. List KBs for user API
3. Get Knowledge base details
4. Download Alterations
Generate Answer
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/role-based-access-control
NEW QUESTION # 33
You have a Microsoft OneDrive folder that contains a 20-GB video file named FileVavi. You need to index File1.avi by using the Azure Video Indexer website. What should you do?
Answer: C
Explanation:
Azure Video Indexer can ingest a video by URL as long as the URL is publicly accessible and directly downloadable (no authentication or HTML landing page). Creating a download link in OneDrive produces a direct-file URL that Video Indexer can fetch and index without you re-uploading the 20-GB file through your browser. A generic OneDrive "sharing link" often lands on a web page (not a direct file) and may require authentication, which Video Indexer cannot crawl. Uploading locally is possible but unnecessary and time- consuming for a 20-GB video.
Microsoft References
Upload/index videos in Video Indexer (supports uploading from a public URL).
https://learn.microsoft.com/azure/azure-video-indexer/upload-index-videos Video Indexer quotas and limits (file size limits; uploading by URL).
NEW QUESTION # 34
......
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