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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Process and index video content - Extract text and handwriting from images - Build and deploy custom vision models - Integrate vision capabilities into applications - Analyze images and detect objects/features |
| Topic 2: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Topic 4: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |
| Topic 5: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Select suitable AI models |
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NEW QUESTION # 195
You have an Azure subscription.
You plan to build a solution that will analyze scanned documents and export relevant fields to a database.
You need to recommend an Azure Al Document Intelligence model for the following types of documents:
. Expenditure request authorization forms
. Structured and unstructured survey forms
. Structured employment application forms
The solution must minimize development effort and costs.
Which of model should you recommend for each document type?
Answer:
Explanation:
Explanation:
To recommend the appropriate Azure AI Document Intelligence models for the given document types while minimizing development effort and costs, here are the choices for each document type:
* Expenditure request authorization forms:
* These forms are likely to be structured with specific fields that need to be extracted. Using a prebuilt model that can handle structured forms will minimize development effort.
* Recommended model: Prebuilt layout (This is because prebuilt layout models are designed to handle a wide range of structured documents.)
* Structured employment application forms:
* These forms are also structured with specific fields such as name, address, job position, etc. Using a prebuilt model that handles structured documents will be efficient.
* Recommended model: Prebuilt layout (Similar reasoning as above, for structured forms.)
* Structured and unstructured survey forms:
* Survey forms can be both structured and unstructured, meaning they may have free-text responses along with structured questions. For such varied content, a custom neural model that can handle both types of data might be the best fit.
* Recommended model: Custom neural (This allows flexibility to handle both structured and unstructured data within the survey forms.) Based on this analysis, the appropriate models are:
* Expenditure request authorization forms: Prebuilt layout
* Structured employment application forms: Prebuilt layout
* Structured and unstructured survey forms: Custom neural
NEW QUESTION # 196
You have 100 chatbots that each has its own Language Understanding model.
Frequently, you must add the same phrases to each model.
You need to programmatically update the Language Understanding models to include the new phrases.
How should you complete the code? 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:
Explanation:
Box 1: AddPhraseListAsync
Example: Add phraselist feature
var phraselistId = await client.Features.AddPhraseListAsync(appId, versionId, new PhraselistCreateObject
{
EnabledForAllModels = false,
IsExchangeable = true,
Name = "QuantityPhraselist",
Phrases = "few,more,extra"
});
Box 2: PhraselistCreateObject
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api
NEW QUESTION # 197
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:
Explanation:
Step 1: Pull the Anomaly Detector container image.
Step 2: Create a custom Dockerfile
Step 3: Build the image
Step 4: Push the image to an Azure container registry.
https://docs.microsoft.com/en-us/azure/cognitive-services/containers/container-reuse-recipe
NEW QUESTION # 198
You are building an image sharing app that will use Azure AI to prevent users from sharing sexually explicit images.
You need to ensure that inappropriate images are identified correctly. The solution must minimize development effort.
What should you use?
Answer: A
Explanation:
You want to prevent users from sharing sexually explicit images and minimize development effort.
Azure AI Content Safety Studio provides a no-code, browser-based experience to test and tune image moderation using the Content Safety service. It supports out-of-the-box classification for Sexual, Hate, Violence, and Self-harm categories, lets you adjust severity thresholds, review results, and export settings or sample code with minimal engineering work.
Other options:
* A. Visual Studio - an IDE, not a moderation service.
* B. Vision Studio in Azure AI Vision - focuses on vision tasks (OCR, image analysis). For policy
/compliance moderation of explicit content, Content Safety is the correct service.
* D. Azure AI Studio - general hub for building generative AI apps; not the specialized moderation experience.
Microsoft References
* Azure AI Content Safety overview and image moderation concepts.
* Content Safety Studio quickstart and scenarios.
* Content categories (Sexual, Hate, Violence, Self-harm).
NEW QUESTION # 199
You are building an app by using the Speech SDK. The app will translate speech from French to German by using natural language processing.
You need to define the source language and the output 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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 200
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