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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Select suitable AI models - Monitor, optimize, and secure AI solutions |
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases |
| Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Extract text and handwriting from images - Analyze images and detect objects/features - Build and deploy custom vision models - Process and index video content |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning |
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NEW QUESTION # 153
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [email@domain.com] You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type 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.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity
NEW QUESTION # 154
You have an Azure subscription that contains an Azure Al Language custom question answering project named QA1.
You need to import question and answer pairs to QA1.
Which two file formats can you use? Each correct answer presents a complete solution NOTE; Each correct selection is worth one point.
Answer: A,C
Explanation:
For an Azure AI Language Custom Question Answering project (QA1), you can import Q & A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft's guidance explicitly states that when importing question-answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q & A pairs in Language Studio. Microsoft Learn Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn Microsoft References:
Move projects and Q & A pairs - "Import question and answers... option to import either an Excel or TSV file." Microsoft Learn Format guidelines for Custom Question Answering - structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn
NEW QUESTION # 155
You are developing a new sales system that will process the video and text from a public-facing website.
You plan to notify users that their data has been processed by the sales system.
Which responsible AI principle does this help meet?
Answer: A
Explanation:
Explanation
"When an AI application relies on personal data, such as a facial recognition system that takes images of people to recognize them; you should make it clear to the user how their data is used and retained, and who has access to it." from: https://docs.microsoft.com/en-us/learn/paths/prepare-for-ai-engineering/
NEW QUESTION # 156
You have a collection of press releases stored as PDF files.
You need to extract text from the files and perform sentiment analysis.
Which service should you use for each task? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 157
You plan to use containerized versions of the Anomaly Detector API on local devices for testing and in on-premises datacenters.
You need to ensure that the containerized deployments meet the following requirements:
Prevent billing and API information from being stored in the command-line histories of the devices that run the container.
Control access to the container images by using Azure role-based access control (Azure RBAC).
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.) NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/containers/container-reuse-recipe
NEW QUESTION # 158
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