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You are examining the Text Analytics output of an application. The text analyzed is: “Our tour guide took us up the Space Needle during our trip to Seattle last week.”
The response contains the data shown in the following table.
Which Text Analytics API is used to analyze the text?
A. Sentiment Analysis
B. Named Entity Recognition
C. Entity Linking
D. Key Phrase Extraction
Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.
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Case Study 2 – Contoso, Ltd.
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.
Contoso has the following subscriptions:
– 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:
Contoso has the intellectual property shown in the following table.
Text-based content is provided only in one language and is not translated.
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
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.
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.
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
Need to specify the field name, and then use < 0.7 to handle trigger if confidence score is less than 70%.
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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 have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You migrate to a Cognitive Search service that uses a higher tier.
Does this meet the goal?
A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.