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QUESTION 107
Case Study 2 – Contoso, Ltd.
General Overview
Contoso, Ltd. is an international accounting company that has offices in France. Portugal, and the United Kingdom. Contoso has a professional services department that contains the roles shown in the following table.
Infrastructure
Contoso has the following subscriptions:
– Azure
– Microsoft 365
– Microsoft Dynamics 365
Azure Active (Azure AD) Directory
Contoso has Azure Active Directory groups for securing role-based access. The company uses the following group naming conventions:
– ICountryJ-[Levell-[Role]
– [Level]-[Role]
Intellectual Property
Contoso has the intellectual property shown in the following table.
Text-based content is provided only in one language and is not translated.
Planned Projects
Contoso plans to develop the following:
– A document processing workflow to extract information automatically from PDFs and images of financial documents
– A customer-support chatbot that will answer questions by using FAQs
– A searchable knowledgebase of all the intellectual property
Technical Requirements
Contoso identifies the following technical requirements:
– All content must be approved before being published.
– All planned projects must support English, French, and Portuguese.
– All content must be secured by using role-based access control (RBAC).
– RBAC role assignments must use the principle of least privilege.
– RBAC roles must be assigned only to Azure Active Directory groups.
– Al solution responses must have a confidence score that is equal to or greater than 70 percent.
– When the response confidence score of an Al response is lower than 70 percent, the response must be improved by human input.
Chatbot Requirements
Contoso identifies the following requirements for the chatbot:
– Provide customers with answers to the FAQs.
– Ensure that the customers can chat to a customer service agent.
– Ensure that the members of a group named Management-Accountants can approve the FAQs.
– Ensure that the members of a group named Consultant-Accountants can create and amend the FAQs.
– Ensure that the members of a group named the Agent-CustomerServices can browse the FAQs.
– Ensure that access to the customer service agents is managed by using Omnichannel for Customer Service.
– When the response confidence score is low.
– Ensure that the chatbot can provide other response options to the customers.
Document Processing Requirements
Contoso identifies the following requirements for document processing:
– The document processing solution must be able to process standardized financial documents that have the following characteristics:
– Contain fewer than 20 pages.
– Be formatted as PDF or JPEG files.
– Have a distinct standard for each office.
– The document processing solution must be able to extract tables and text from the financial documents.
– The document processing solution must be able to extract information from receipt images.
– Members of a group named Management-Bookkeeper must define how to extract tables from the financial documents.
– Members of a group named Consultant-Bookkeeper must be able to process the financial documents.
Knowledgebase Requirements
Contoso identifies the following requirements for the knowledgebase:
– Supports searches for equivalent terms
– Can transcribe jargon with high accuracy
– Can search content in different formats, including video
– Provides relevant links to external resources for further research
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 returns 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?
A. documentResults.docType == “prebuilt:receipt”
B. documentResults.fields.”.confidence < 0.7
C. documentResults.fields.ReceiptType.confidence > 0.7
D. documentResults.fields.MerchantName.confidence < 0.7
Answer: C
Explanation:
Need to specify the field name, and then use < 0.7 to handle trigger if confidence score is less than 70%.
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/api-v2-0/reference-sdk-api-v2-0
