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| Section | Weight | Objectives |
|---|---|---|
| Implement Generative AI Solutions | 10-15% | - Implement prompt engineering techniques - Apply responsible AI practices - Integrate Azure OpenAI models into applications - Use Azure OpenAI Service for generative AI |
| Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Integrate Azure AI services into applications - Design and implement decision automation |
| Implement Natural Language Processing Solutions | 20-25% | - Build question answering solutions - Translate text and speech - Implement speech capabilities using Azure AI Speech - Create conversational AI solutions with Azure AI Bot Service - Analyze text using Azure AI Language |
| Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Manage AI costs and resource allocation - Plan and configure secure AI solutions - Select appropriate Azure AI services and technologies |
| Implement Computer Vision Solutions | 15-20% | - Read and process documents using Azure AI Document Intelligence - Implement Azure AI Face service - Analyze images using Azure AI Vision - Implement object detection and image classification - Process video and generate insights |
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement semantic and vector search - Create custom skills for Azure AI Search - Implement Azure AI Search solutions - Design document processing pipelines |
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NEW QUESTION # 31
You have an Azure subscription that contains an Azure OpenAI resource named AH.
You build a chatbot that will use AI1 to provide generative answers to specific questions.
You need to ensure that the responses are more creative and less deterministic.
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:
NEW QUESTION # 32
You are building a Conversational Language Understanding model for an e-commerce platform.
You need to construct an entity to capture billing addresses.
Which entity type should you use for the billing address?
Answer: E
Explanation:
An ML entity can be composed of smaller sub-entities, each of which can have its own properties.
For example, Address could have the following structure:
Address: 4567 Main Street, NY, 98052, USA
Building Number: 4567
Street Name: Main Street
State: NY
Zip Code: 98052
Country: USA
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-entity-types
NEW QUESTION # 33
You run the following command.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation
Text Description automatically generated
Box 1: Yes
http://localhost:5000/status: Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query.
Box 2: Yes
The command saves container and LUIS logs to output mount at C:\output, located on container host Box 3: Yes
http://localhost:5000/swagger: The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 34
You are developing the smart e-commerce project.
You need to implement autocompletion as part of the Cognitive Search solution.
Which three actions should you perform? Each correct answer presents part of the solution. (Choose three.) NOTE: Each correct selection is worth one point.
Answer: B,C,F
Explanation:
Scenario: Support autocompletion and autosuggestion based on all product name variants.
Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.
POST /indexes/myxboxgames/docs/autocomplete?search&api-version=2020-06-30
{
"search": "minecraf",
"suggesterName": "sg"
}
In Azure Cognitive Search, typeahead or "search-as-you-type" is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for "Seattle" will have prefix combinations of "sea", "seat", "seatt", and "seattl" to support typeahead.
Reference:
https://docs.microsoft.com/en-us/azure/search/index-add-suggesters
Topic 2, Contoso, Ltd.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab. note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
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
NEW QUESTION # 35
You are developing a text processing solution.
You develop the following method.
You call the method by using the following code.
GetKeyPhrases(textAnalyticsClient, "the cat sat on the mat");
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
The Key Phrase Extraction API evaluates unstructured text, and for each JSON document, returns a list of key phrases.
Box 2: No
'the' is not a key phrase.
This capability is useful if you need to quickly identify the main points in a collection of documents. For example, given input text "The food was delicious and there were wonderful staff", the service returns the main talking points: "food" and "wonderful staff".
Box 3: No
Key phrase extraction does not have confidence levels.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-keyword-
NEW QUESTION # 36
......
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