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| Section | Objectives |
|---|---|
| Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
| Implement Knowledge Mining and Document Intelligence | - Enrich and index data for search scenarios - Implement document intelligence solutions - Build Azure AI Search solutions |
| Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services |
| Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Plan solution requirements and architecture - Manage security, compliance, and responsible AI |
| Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Extract information using OCR and form recognition - Implement custom vision models |
You can use this Microsoft simulation software without an internet connection after installation. Tracking and reporting features of our Designing and Implementing a Microsoft Azure AI Solution AI-102 Practice Exam software makes it easier for you to identify and overcome mistakes. Customization feature of this format allows you to change time limits and questions numbers of mock exams.
NEW QUESTION # 407
You are building a chatbot by using the Microsoft Bot Framework SDK.
You use an object named UserProfile to store user profile information and an object named ConversationData to store information related to a conversation.
You create the following state accessors to store both objects in state.
var userStateAccessors = _userState.CreateProperty<UserProfile>(nameof(UserProfile)); var conversationStateAccessors = _conversationState.CreateProperty<ConversationData>(nameof(ConversationData)); The state storage mechanism is set to Memory Storage.
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:
Explanation:
Box 1: Yes
You create property accessors using the CreateProperty method that provides a handle to the BotState object. Each state property accessor allows you to get or set the value of the associated state property.
Box 2: Yes
Box 3: No
Before you exit the turn handler, you use the state management objects' SaveChangesAsync() method to write all state changes back to storage.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-v4-state
NEW QUESTION # 408
You need to develop an extract solution for the receipt images. The solution must meet the document processing requirements and the technical requirements.
You upload the receipt images to the From Recognizer API for analysis, and the API ret urns the following JSON.
Which expression should you use to trigger a manual review of the extracted information by a member of the Consultant-Bookkeeper group?
Answer: D
Explanation:
The requirements state:
* All AI solution responses must have a confidence score # 70%.
* If the response confidence score is < 70%, the response must be improved by human input.
* Members of the Consultant-Bookkeeper group must be able to process financial documents, which includes performing manual review when the AI confidence is below threshold.
Looking at the provided JSON:
" fields " : {
" ReceiptType " : {
" type " : " string " ,
" valueString " : " Itemized " ,
" confidence " : 0.672
},
" MerchantName " : {
" type " : " string " ,
" valueString " : " Tailwind " ,
" confidence " : 0.913
}
}
* ReceiptType.confidence = 0.672 # this is below 0.7.
* MerchantName.confidence = 0.913 # this is above 0.7.
Therefore, the correct condition for triggering manual review is:
documentResults.fields. < field > .confidence < 0.7
Now, evaluating the options:
* A. documentResults.docType == " prebuilt:receipt "
* Always true for receipts, does not check confidence. Not correct.
* B. documentResults.fields.*.confidence < 0.7
* This is the correct general expression: trigger manual review whenever any field confidence is below 0.7.
* C. documentResults.fields.ReceiptType.confidence > 0.7
* This would bypass manual review when ReceiptType has high confidence. The requirement is to trigger review when confidence < 0.7, so this is the opposite.
* D. documentResults.fields.MerchantName.confidence < 0.7
* This only checks one field (MerchantName). In the JSON, MerchantName has confidence 0.913 (
> 0.7), so this condition would not trigger, but ReceiptType clearly needs review. Too narrow.
answer: B. documentResults.fields.*.confidence < 0.7
* Azure AI Document Intelligence - Confidence scores
* Human-in-the-loop for Document Intelligence
Microsoft References
NEW QUESTION # 409
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,F
Explanation:
This question is about selecting valid training files for a custom Form Recognizer model in Azure AI Document Intelligence (formerly Form Recognizer).
According to Microsoft documentation, Form Recognizer supports the following for training and analysis:
* PDF (# 500 pages, # 50 MB for training)
* JPEG/JPG (# 50 MB)
* PNG (# 50 MB)
* TIFF (# 50 MB)
Unsupported:
* MP4 # Video not supported.
* GIF # Not supported.
* Images (JPG, PNG, TIFF): # 50 MB
* PDFs: # 50 MB
* Note: 500 pages limit applies to PDFs.
* File1 - PDF - 20 MB
* Type supported.
* Size within limit (< 50 MB).
* Valid #
* File2 - MP4 - 100 MB
* MP4 is not supported.
* Invalid #
* File3 - JPG - 20 MB
* Type supported.
* Size within limit (< 50 MB).
* Valid #
* File4 - PDF - 100 MB
* Type supported.
* Size exceeds the 50 MB maximum limit.
* Invalid #
* File5 - GIF - 1 MB
* GIF format not supported.
* Invalid #
* File6 - JPG - 40 MB
* Type supported.
* Size within limit (< 50 MB).
* Valid #
The answer:
A). File1, C. File3, F. File6
* Azure AI Document Intelligence (Form Recognizer) - Supported file formats
* Train a custom model with Azure Form Recognizer
NEW QUESTION # 410
Hotspot Question
You have an Azure subscription that contains an Azure AI Content Safety resource.
You are building a social media messaging app.
You need to build a solution that will analyze messages and flag messages that contain explicit content.
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:
Box 1: AnalyzeTextOptions
Analyze text content (C#)
Get started with the Content Safety Studio, REST API, or client SDKs to do basic text moderation. The Azure AI Content Safety service provides you with AI algorithms for flagging objectionable content.
Box 2: client.AnalyzeText(request)
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/quickstart-text
NEW QUESTION # 411
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:
Reference:
https://github.com/solliancenet/tech-immersion-data-ai/blob/master/ai-exp1/README.md
NEW QUESTION # 412
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