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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Process and index video content - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images |
| Topic 2: 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 3: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles |
| Topic 4: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
| Topic 5: Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval |
| Topic 6: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Implement model monitoring and feedback - Deploy and manage generative models - Apply prompt engineering and fine-tuning |
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질문 # 237
You need to implement a table projection to generate a physical expression of an Azure Cognitive Search index.
Which three properties should you specify in the skillset definition JSON configuration table node? Each correct answer presents part of the solution. (Choose three.) NOTE: Each correct selection is worth one point.
정답：C,D,E
설명：
For a table projection in a knowledge store (skillset definition), the table node must define:
* tableName: the table to project into (physical expression).
* generatedKeyName: the name of the system-generated key column.
* source: an object-shaping expression (projection path) describing what document content flows into the table.dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.
References
* Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.
질문 # 238
You are building an app that will translate speech by using the Azure Al Language service.
You need configure the app to translate the speech from English to Italian.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 239
You are developing the smart e-commerce project.
You need to design the skillset to include the contents of PDFs in searches.
How should you complete the skillset design diagram? To answer, drag the appropriate services to the correct stages. Each service 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.
정답：
설명：
Explanation
Box 1: Azure Blob storage
At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.
Box 2: Computer Vision API
Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.
The Computer Vision Read API is Azure's latest OCR technology (learn what's new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.
Box 3: Translator API
Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.
Box 4: Azure Files
Scenario: Store all raw insight data that was generated, so the data can be processed later.
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-concept-intro
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
질문 # 240
You use the Custom Vision service to build a classifier.
After training is complete, you need to evaluate the classifier.
Which two metrics are available for review? Each correct answer presents a complete solution. (Choose two.) NOTE: Each correct selection is worth one point.
정답：C,E
설명：
The question is about evaluating a Custom Vision classifier after training.
When you train an image classifier in Azure Custom Vision, the service automatically calculates performance metrics to help you evaluate the quality of the model.
* Precision:
* The percentage of correct positive predictions out of all positive predictions made.
* Formula: Precision = TP / (TP + FP)
* Helps determine how reliable positive predictions are.
* Recall:
* The percentage of actual positives that were correctly predicted.
* Formula: Recall = TP / (TP + FN)
* Helps determine how many of the true positives were captured.
Metrics Provided by Custom Vision:These two metrics are explicitly shown in the Custom Vision portal and via API.
* A. Recall
* Yes, Custom Vision reports recall.
* Correct
* B. F-score
* Not directly reported in the Custom Vision portal.
* Although it can be derived from precision and recall, it is not provided as a direct metric.
* Incorrect
* C. Weighted accuracy
* Not reported by Custom Vision.
* Incorrect
* D. Precision
* Yes, Custom Vision reports precision.
* Correct
* E. Area under the curve (AUC)
* Not reported by Custom Vision.
* More common in ROC curve analysis, not part of Custom Vision output.
* Incorrect
Option Analysis
The answer: A. recall, D. precision
* Evaluate the prediction performance of your classifier in Custom Vision
* Custom Vision training and evaluation
질문 # 241
You are developing an application that includes language translation.
The application will translate text retrieved by using a function named get_text_to_be_translated. The text can be in one of many languages. The content of the text must remain within the Americas Azure geography.
You need to develop code to translate the text to a single language.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 242
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