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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models - Process and index video content |
| Topic 2: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning |
| Topic 3: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots |
| Topic 4: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
| Topic 5: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select suitable AI models - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources |
| Topic 6: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
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NEW QUESTION # 298
You are building a chatbot.
You need to use the Content Moderator service to identify messages that contain sexually explicit language.
Which section in the response from the service will contain the category score, and which category will be assigned to the message? To answer, select the appropriate options in the answer area, NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 299
You are building an Azure Cognitive Search custom skill.
You have the following custom skill schema definition.
For each of the following statements, select Yes if the statement. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-output-field-mapping
NEW QUESTION # 300
You are building an app that will scan confidential documents and use the Language service to analyze the contents.
You provision an Azure Cognitive Services resource.
You need to ensure that the app can make requests to the Language service endpoint. The solution must ensure that confidential documents remain on-premises.
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.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation
You need to analyze confidential documents with the Language service while ensuring those documents never leave your premises. The supported pattern for Azure AI Services (Cognitive Services) is to run the Language service container on-premises and send the app's requests to that local container endpoint. The container performs inference locally; only metering/billing calls are sent to Azure.
To achieve this:
Provision an on-premises Kubernetes cluster that has internet connectivity.
Azure AI service containers running on-prem require outbound access to Azure for metering and billing unless you're using the special disconnected model (not implied here). Therefore, the cluster must have internet connectivity so the container can reach the Azure billing endpoint while keeping document content local.
Pull an image from the Microsoft Container Registry (MCR).
Official Azure AI service containers (including Language) are published in MCR, not Docker Hub. You must pull the Language (Text Analytics/Language) container image from MCR.
Run the container and specify an API key and the Endpoint URL of the Cognitive Services resource.
When you start the container, you configure it with the API key and billing (endpoint) URL of your Azure Cognitive Services (multi-service or Language) resource. This enables the container to authenticate and report usage to Azure, while your data stays on-prem.
Why not the other options?
AKS would place workloads in Azure, which isn't required when the requirement is to keep documents on- prem.
On-prem cluster isolated from the internet would block the container's required calls to Azure's billing endpoint.
Pull from Docker Hub is incorrect because Azure AI service containers are published to MCR.
App ID and Client Secret are not the standard parameters for Azure AI service containers; they require API key + billing endpoint.
Microsoft Azure AI Solution References
Use Azure AI services in containers (billing/endpoint and API key requirements, data remains local):
Microsoft Learn - Install and run containers for Azure AI services and Use containers with Azure AI services (metering and billing) .
https://learn.microsoft.com/azure/ai-services/containers/
Language service container images on Microsoft Container Registry (MCR):
Microsoft Learn - Deploy language containers (Text Analytics/Language) .
https://learn.microsoft.com/azure/ai-services/language-service/how-to/use-containers Networking requirements (internet connectivity for metering):
Microsoft Learn - Configure containers and network access for Azure AI services containers .
https://learn.microsoft.com/azure/ai-services/containers/container-requirements-and-limits These documents collectively show that: (a) containers are pulled from MCR, (b) they are configured with API key + endpoint for billing, and (c) internet egress is required for metering while payload data remains on- premises.
NEW QUESTION # 301
Hotspot Question
You have an Azure subscription that contains an Azure AI Language resource named Resource1.
You run the following cURL command, and then play the Output.mp3 file.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth point.
Answer:
Explanation:
Explanation:
Box 1: Yes
There are three voice elements, each with a different voice.
Note: Use voice elements
At least one voice element must be specified within each SSML speak element. This element determines the voice that's used for text to speech.
You can include multiple voice elements in a single SSML document. Each voice element can specify a different voice. You can also use the same voice multiple times with different settings, such as when you change the silence duration between sentences.
Box 2: No
There are two accents only: two voices using en-US, and one voice using en-GB.
Note: Adjust speaking languages
By default, multilingual voices can autodetect the language of the input text and speak in the language of the default locale of the input text without using SSML. Optionally, you can use the
<lang xml:lang> element to adjust the speaking language for these voices to set the preferred accent such as en-GB for British English.
Box 3: No
The third voice is configured with style advertisement_upbeat and styledegree set to 2.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-synthesis-markup- voice
NEW QUESTION # 302
You have a custom analyzer named analyzer1 that performs the following functions
* The transcription of video content
* The extraction of key frames from videos
You run the following command.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 303
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