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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language |
| Implement Knowledge Mining and Document Intelligence | - Implement document intelligence solutions - Enrich and index data for search scenarios - Build Azure AI Search solutions |
| Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Manage security, compliance, and responsible AI - Select appropriate Azure AI services |
| Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Implement custom vision models - Extract information using OCR and form recognition |
| Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Integrate prompt engineering techniques - Build retrieval-augmented generation (RAG) solutions |
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NEW QUESTION # 67
You plan to use Microsoft Foundry to implement a Retrieval Augmented Generation (RAG) pattern.
You have grounding data stored in an Azure Files share.
You need to configure a Microsoft Foundry project to ensure that a model can use the grounding data.
Which actions should you perform in sequence? To answer, drag the appropriate actions to the correct order.
Each action 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:
Step 1: Create a project. (Given)
Step 2: Add a data connection.
Step 3: Add a deployment.
Explanation:
Add a data connection: Before the model can " see " your grounding data stored in an Azure Files share, you must establish a connection. This step allows the project to securely access and index the external data source so it can be used for retrieval.
Add a deployment: In Microsoft Foundry, you don ' t just " add a model " in a vacuum; you must deploy a specific model (such as a GPT-4 chat model or an embedding model) to make it active and available to interact with your data and respond to queries.
NEW QUESTION # 68
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 69
You are building a multilingual chatbot.
You need to send a different answer for positive and negative messages.
Which two Text Analytics APIs should you use? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
B: The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as
"negative", "neutral" and "positive") and confidence scores at the sentence and document-level.
D: The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is unknown. Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- sentiment-analysis?tabs=version-3-1
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- language-detection
NEW QUESTION # 70
You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.
How should you complete the body of the HTTP request? 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.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
NEW QUESTION # 71
You are building a bot and that will use Language Understanding.
You have a LUDown file that contains the following content.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application, email Description automatically generated
Reference:
https://github.com/solliancenet/tech-immersion-data-ai/blob/master/ai-exp1/README.md
NEW QUESTION # 72
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