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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Generative AI Solutions | 10-15% | - Implement prompt engineering techniques - Use Azure OpenAI Service for generative AI - Apply responsible AI practices - Integrate Azure OpenAI models into applications |
| Topic 2: Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation - Plan and configure secure AI solutions |
| Topic 3: Implement Natural Language Processing Solutions | 20-25% | - Create conversational AI solutions with Azure AI Bot Service - Implement speech capabilities using Azure AI Speech - Translate text and speech - Build question answering solutions - Analyze text using Azure AI Language |
| Topic 4: Implement Computer Vision Solutions | 15-20% | - Read and process documents using Azure AI Document Intelligence - Implement object detection and image classification - Process video and generate insights - Analyze images using Azure AI Vision - Implement Azure AI Face service |
| Topic 5: Implement Decision Support Solutions | 10-15% | - Integrate Azure AI services into applications - Monitor and optimize decision support solutions - Design and implement decision automation |
| Topic 6: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Design document processing pipelines - Implement Azure AI Search solutions - Implement semantic and vector search - Create custom skills for Azure AI Search |
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NEW QUESTION # 13
You have data saved in the following format.
Which format was used?
Answer: C
NEW QUESTION # 14
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 # 15
You have a Custom Vision service project that performs object detection. The project uses the General domain for classification and contains a trained model.
You need to export the model for use on a network that is disconnected from the internet.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list o' actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Change Domains to General (compact).
Retain the model.
Export the model.
You have a Custom Vision project that performs object detection. The project currently uses the General domain. You need to export the trained model for offline use (on a disconnected network).
Steps required:
* Change Domains to General (compact).
* Export is only supported with a Compact domain.
* The General (compact) domain is optimized for exporting to devices (e.g., ONNX, TensorFlow, CoreML, etc.).
* If you stay in "General" domain, you cannot export.
* Retain the model.
* After switching domains, you must retrain the model in the new compact domain to generate updated weights.
* Export the model.
* Once retrained in a compact domain, you can export the model for offline deployment.
* Export formats include ONNX, TensorFlow, CoreML, or Docker container, depending on use case.
* Create a new classification model # Not needed; you already have an object detection model.
* Change the classification type # Incorrect; the problem is with the domain type, not classification type.
* Change Domains to General (compact).
* Retain the model.
* Export the model.
* Export a model from Custom Vision
* Domains in Custom Vision
NEW QUESTION # 16
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 17
You build a Language Understanding model by using the Language Understanding portal.
You export the model as a JSON file as shown in the following sample.
To what does the Weather.Historic entity correspond in the utterance?
Answer: D
Explanation:
Let's carefully analyze the JSON output from the exported LUIS (Language Understanding) model.
The utterance is:
" average amount of rain by month at chicago last year "
The extracted entities are:
Weather.WeatherRange # " average " (positions 0-6)
Weather.WeatherCondition # " rain " (positions 18-21)
Weather.Historic # " by month " (positions 23-30)
We need to determine what text fragment corresponds to Weather.Historic .
Looking at the " startPos " : 23, " endPos " : 30 , these indexes map to the substring " by month " in the utterance.
Why not the other options?
B). chicago # Refers to a location entity, but the entity name is Weather.Location (not shown here).
C). rain # Already captured as Weather.WeatherCondition .
D). location # Would also be "chicago," but again that's a different entity type.
So the only correct match for Weather.Historic is " by month " .
The Answer: A. by month
Microsoft References
Entities in LUIS
Export LUIS model schema
Labeling utterances with entities
NEW QUESTION # 18
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