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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications - Build and deploy custom vision models - Extract text and handwriting from images |
| Topic 2: 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 3: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service |
| Topic 4: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Customize and deploy NLP models |
| Topic 5: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Implement model monitoring and feedback |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Select suitable AI models - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
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NEW QUESTION # 123
You are using a Language Understanding service to handle natural language input from the users of a web- based customer agent.
The users report that the agent frequently responds with the following generic response: "Sorry, I don't understand that." You need to improve the ability of the agent to respond to requests.
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:
Explanation:
- enable active learning
- validate the utterances
- train and republish
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint-utterances#log- user-queries-to-enable-active-learning
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-prebuilt-model
NEW QUESTION # 124
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.
Answer:
Explanation:
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
NEW QUESTION # 125
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You develop an application to identify species of flowers by training a Custom Vision model. You receive images of new flower species.
You need to add the new images to the classifier.
Solution: You add the new images, and then use the Smart Labeler tool.
Does this meet the goal?
Answer: B
Explanation:
The model need to be extended and retrained.
Note: Smart Labeler to generate suggested tags for images. This lets you label a large number of images more quickly when training a Custom Vision model.
NEW QUESTION # 126
You are planning the product creation project.
You need to build the REST endpoint to create the multilingual product descriptions.
How should you complete the URI? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-translate
NEW QUESTION # 127
What is the primary purpose of a data warehouse?
Answer: C
Explanation:
* A data warehouse is designed for analytical workloads (OLAP), not transactional workloads (OLTP).
* It integrates data from multiple sources and supports complex queries, aggregations, and reporting for decision-making.
* A. storage for transactional LOB apps # describes OLTP (e.g., SQL Server, Azure SQL Database), not a data warehouse.
* B. transformation services # describes ETL/ELT pipelines (e.g., Data Factory), not the warehouse itself.
* C. read-only storage of historical data # partially true but too narrow; the key feature is analytics, not just storage.
* D. answers to complex queries across multiple sources # Correct and complete definition.
The answer: D
Reference: What is a data warehouse?
NEW QUESTION # 128
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