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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Analyze images and videos using Azure AI Vision - Implement custom vision models |
| Topic 2: Implement Natural Language Processing Solutions | - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services - Implement question answering and conversational AI |
| Topic 3: Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Manage security, compliance, and responsible AI - Select appropriate Azure AI services |
| Topic 4: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Implement Azure OpenAI Service solutions - Integrate prompt engineering techniques |
| Topic 5: Implement Knowledge Mining and Document Intelligence | - Enrich and index data for search scenarios - Implement document intelligence solutions - Build Azure AI Search solutions |
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NEW QUESTION # 200
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 # 201
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:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app's package from LUIS portal
The versioned app's package is available from the Versions list page.
Sign on to the LUIS portal.
Select the app in the list.
Select Manage in the app's navigation bar.
Select Versions in the left navigation bar.
Select the checkbox to the left of the version name in the list.
Select the Export item from the contextual toolbar above the list.
Select Export for container (GZIP).
The package is downloaded from the browser.
Step 2: Select v1.1 of app1.
A trained or published app packaged as a mounted input to the container with its associated App ID.
Step 3: Run a contain and mount the model file.
Run the container, with the required input mount and billing settings.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 202
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.
* 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 # 203
You have a Computer Vision resource named contoso1 that is hosted in the West US Azure region.
You need to use contoso1 to make a different size of a product photo by using the smart cropping feature.
How should you complete the API URL? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 204
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:
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 # 205
......
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