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| Section | Weight | Objectives |
|---|---|---|
| Implement Natural Language Processing Solutions | 20-25% | - Create conversational AI solutions with Azure AI Bot Service - Analyze text using Azure AI Language - Build question answering solutions - Translate text and speech - Implement speech capabilities using Azure AI Speech |
| Plan and Manage an Azure AI Solution | 15-20% | - Plan and configure secure AI solutions - Implement governance and compliance for AI solutions - Manage AI costs and resource allocation - Select appropriate Azure AI services and technologies |
| Implement Computer Vision Solutions | 15-20% | - Read and process documents using Azure AI Document Intelligence - Process video and generate insights - Implement Azure AI Face service - Implement object detection and image classification - Analyze images using Azure AI Vision |
| Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Integrate Azure AI services into applications - Design and implement decision automation |
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Create custom skills for Azure AI Search - Implement Azure AI Search solutions - Implement semantic and vector search - Design document processing pipelines |
| Implement Generative AI Solutions | 10-15% | - Implement prompt engineering techniques - Integrate Azure OpenAI models into applications - Use Azure OpenAI Service for generative AI - Apply responsible AI practices |
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NEW QUESTION # 13
You have an Azure subscription that contains an Azure OpenAI resource named All and an Azure Al Content Safety resource named CS1.
You build a chatbot that uses All to provide generative answers to specific questions and CS1 to check input and output for objectionable content.
You need to optimize the content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Safety metaprompt feature to run the tests Does this meet the requirement?
Answer: B
NEW QUESTION # 14
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following list of phrases to use for training.
Find contacts in London. Who do I know in Seattle?
Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new intent for location.
Does this meet the goal?
Answer: B
Explanation:
The scenario involves a Language Understanding (LUIS) or Conversational Language Understanding (CLU) model that has:
* An intent called FindContact (used for searching a contact list).
* Training phrases like:
* "Find contacts in London"
* "Who do I know in Seattle?"
* "Search for contacts in Ukraine"
Key Requirement The model must recognize locations (London, Seattle, Ukraine) inside the FindContact intent.
* The proposed solution is to create a new intent for location.
* This is not correct because:
* Intents represent what the user wants to do (the action, e.g., FindContact ).
* Entities represent details within the user's utterance (the data, e.g., location ).
* Here, the intent is already defined: FindContact .
* Location should be modeled as an entity (Location entity), not as a separate intent.
Why the Solution is Incorrect Creating a new intent for each location (London, Seattle, Ukraine) would fragment the model and make it harder to generalize. Instead, the model should capture location as a parameter of the single intent FindContact .
* Keep the existing FindContact intent.
* Add an entity for location (prebuilt GeographyV2 in LUIS or "Location" entity in CLU).
* Train the model so that London , Seattle , Ukraine are recognized as entity values inside the FindContact intent.
Correct Approach
* Intents and entities in Language Understanding
* Prebuilt entity: GeographyV2
Microsoft References
NEW QUESTION # 15
You build a custom Form Recognizer model.
You receive sample files to use for training the model as shown in the following table.
Which three files can you use to train the model? Each correct answer presents a complete solution. (Choose three.) NOTE: Each correct selection is worth one point.
Answer: A,C,D
Explanation:
Input requirements
Form Recognizer works on input documents that meet these requirements:
Format must be JPG, PNG, PDF (text or scanned), or TIFF. Text-embedded PDFs are best because there's no possibility of error in character extraction and location.
File size must be less than 50 MB.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview
NEW QUESTION # 16
You are building a solution that students will use to find references for essays. You use the following code to start building the solution.
For each of the following statements, select Yes is the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 17
Drag and Drop Question
You have a question answering project in Azure AI Language.
You need to move the project to a Language service instance in a different Azure region.
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:
https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/question- answering/how-to/migrate-knowledge-base
NEW QUESTION # 18
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