Explanation:
To deploy the bot to Azure, you should run the following three Azure CLI commands in sequence:
az deployment group create This command will create the Azure resources for your bot using an ARM template and a parameters file. You need to specify the resource group name, the template file path, and the parameters file path. For example:
az deployment group create --resource-group myResourceGroup --template-file
"deploymentTemplates\template-with-preexisting-rg.json" --parameters "deploymentTemplates\parameters- for-template-BotApp-with-rg.json" This command will also output the app ID and password of your bot, which you will need for the next command1.
az webapp deployment source config-zip This command will deploy your bot code to the app service that you created in the previous step. You need to specify the resource group name, the app service name, and the zip file path of your bot code. For example:
az webapp deployment source config-zip --resource-group myResourceGroup --name myBotAppService --src
"code.zip"
This command will also output the URL of your bot endpoint, which you will need for the next command2.
az ad app update This command will update your bot registration with the endpoint URL of your bot. You need to specify the app ID of your bot and the endpoint URL. For example:
az ad app update --id myBotAppId --set replyUrls="https://myBotAppService.azurewebsites.net/api
/messages"
This command will complete the deployment process and make your bot ready to be tested3.

Topic 1, Contoso, Ltd.Case Study
This is a case study Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
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

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

Comprehensive Detailed Explanation
When you develop a custom question answering project (formerly QnA Maker, now part of Azure Cognitive Service for Language - Question Answering), you can configure it for multi-turn conversations.
Follow-up prompts
Used to create multi-turn conversations.
They allow the system to present additional questions to the user after an initial question, guiding the conversation flow.
Example:
User: " Tell me about Azure Storage. "
Bot: " Which type of storage: Blob, File, Queue, or Table? "
These follow-up options are configured using follow-up prompts.
Correct choice for this question.
Active learning
Helps improve the knowledge base by suggesting alternative questions from user queries.
Not related to multi-turn conversation flow.
Alternate questions
Used to add different phrasings for the same question.
Example: "What is Azure Blob Storage?" and "Explain Blob Storage in Azure." This improves recognition but does not enable multi-turn conversations.
Chit-chat
Adds small talk and casual conversation handling (e.g., greetings, jokes).
Not related to multi-turn Q & A about a knowledge base.
The Answer: A. Add follow-up prompts
Microsoft References
Multi-turn conversations in Question Answering
Add follow-up prompts in Question Answering

Explanation:

To generate less deterministic and more creative responses with Azure OpenAI chat completions, increase the Temperature setting. Temperature controls sampling randomness: higher values (e.g., ~0.8-1.0) yield more diverse/creative outputs; lower values (~0-0.2) are more deterministic.
The message being sent in the Messages collection is the user's prompt, so the correct role is ChatRole.User .
The role does not control creativity; it only tells the model who authored the message (system/assistant/user
/function). Creativity is governed by parameters such as Temperature (and, to a lesser extent, TopP ).
PresencePenalty biases against repeating seen tokens/topics but does not primarily increase creativity in the intended way.
So the code should look like:
new ChatCompletionsOptions()
{
Messages =
{
new ChatMessage(ChatRole.User, @ " < user prompt here > " ),
},
Temperature = ( float ) 1.0 ,
MaxTokens = 800 ,
};
Microsoft Azure AI Solution References
Azure OpenAI Service - Chat Completions parameters (Temperature controls randomness/creativity; higher = more diverse results).
Azure OpenAI Service - Message roles in chat ( system , user , assistant , function ) and their purposes.
Azure OpenAI Service - Presence/Frequency penalties vs. Temperature (penalties reduce repetition; temperature changes creativity).