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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Test, deploy, and optimize agents |
| Topic 2: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots |
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Topic 4: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Deploy and manage generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models |
| Topic 5: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - 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 - Select suitable AI models - Plan solutions aligned with responsible AI principles |
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154. Frage
You have an Azure Al Speech service resource named Resource1.
You call Resource1 by running the following Python code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth point.
Antwort:
Begründung:
155. Frage
You plan to provision Azure AI service resources by using the following method.
Antwort:
Begründung:
Explanation:
156. Frage
You have 100 chatbots that each has its own Language Understanding model.
Frequently, you must add the same phrases to each model.
You need to programmatically update the Language Understanding models to include the new phrases.
How should you complete the code? 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.
Antwort:
Begründung:
Explanation:
Box 1: AddPhraseListAsync
Example: Add phraselist feature
var phraselistId = await client.Features.AddPhraseListAsync(appId, versionId, new PhraselistCreateObject
{
EnabledForAllModels = false,
IsExchangeable = true,
Name = "QuantityPhraselist",
Phrases = "few,more,extra"
});
Box 2: PhraselistCreateObject
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api
157. Frage
You are planning the product creation project.
You need to recommend a process for analyzing videos.
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. (Choose four.)
Antwort:
Begründung:
Explanation:
Scenario: All videos must have transcripts that are associated to the video and included in product descriptions.
Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.
Step 1: Upload the video to blob storage
Given a video or audio file, the file is first dropped into a Blob Storage. T Step 2: Index the video by using the Video Indexer API.
When a video is indexed, Video Indexer produces the JSON content that contains details of the specified video insights. The insights include: transcripts, OCRs, faces, topics, blocks, etc.
Step 3: Extract the transcript from the Video Indexer API.
Step 4: Translate the transcript by using the Translator API.
Reference:
https://azure.microsoft.com/en-us/blog/get-video-insights-in-even-more-languages/
https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-output-json-v2
158. Frage
You have a factory that produces cardboard packaging for food products. The factory has intermittent internet connectivity.
The packages are required to include four samples of each product.
You need to build a Custom Vision model that will identify defects in packaging and provide the location of the defects to an operator. The model must ensure that each package contains the four products.
Which project type and domain should you use? To answer, drag the appropriate options to the correct targets.
Each option 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.
Antwort:
Begründung:
Explanation:
Project type: Object detection
Domain: General (compact)
The scenario requires building a Custom Vision model for a factory with intermittent internet connectivity.
The solution must:
Detect defects in packaging
Locate the defects (provide bounding box information)
Ensure each package contains four products
Support offline use due to intermittent connectivity
Step 1 - Determine Project Type
Image Classification: Assigns a label to an image, but cannot locate where objects/defects are in the image.
Object Detection: Identifies what objects are present and their location (via bounding boxes).
Since the requirement is to identify location of defects and count products, the correct choice is Object detection.
Step 2 - Determine Domain
General domain: Standard general-purpose domain, optimized for cloud inference.
General (compact) domain: A lightweight version designed for exporting models to run offline on edge devices (iOS, Android, ONNX, etc.). Perfect for scenarios with intermittent internet connectivity.
Food domain: Specialized for food image classification, not defect detection in packaging.
Logo domain: Specialized for logo recognition, irrelevant here.
Since the requirement includes offline capability, the correct domain is General (compact).
Project type: Object detection
Domain: General (compact)
Microsoft References
Custom Vision project types: Classification vs Object detection
Domains in Custom Vision (General, General compact, Food, Logo, etc.)
Exporting compact models for offline use
159. Frage
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