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AI-102 Practice Test


Page 3 out of 51 Pages

Topic 2: 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
roles shown in the following table.

• 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 are developing the knowledgebase.
You use Azure Video Analyzer for Media (previously Video indexer) to obtain transcripts of
webinars.
You need to ensure that the solution meets the knowledgebase requirements.
What should you do?


A.

Create a custom language model


B.

Configure audio indexing for videos only


C.

Enable multi-language detection for videos


D.

Build a custom Person model for webinar presenters





B.
  

Configure audio indexing for videos only



Explanation:
Can search content in different formats, including video
Audio and video insights (multi-channels). When indexing by one channel, partial result for
those models will be available.
Keywords extraction: Extracts keywords from speech and visual text.
Named entities extraction: Extracts brands, locations, and people from speech and visual
text via natural language processing (NLP).
Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC
taxonomy is included.
Artifacts: Extracts rich set of "next level of details" artifacts for each of the models.
Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and
visual text.
Reference:
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-mediadocs/
video-indexer-overview

You are developing the knowledgebase by using Azure Cognitive Search.
You need to process wiki content to meet the technical requirements.
What should you include in the solution?


A.

an indexer for Azure Blob storage attached to a skillset that contains the language
detection skill and the text translation skill


B.

an indexer for Azure Blob storage attached to a skillset that contains the language
detection skill


C.

an indexer for Azure Cosmos DB attached to a skillset that contains the document
extraction skill and the text translation skill


D.

an indexer for Azure Cosmos DB attached to a skillset that contains the language
detection skill and the text translation skill





C.
  

an indexer for Azure Cosmos DB attached to a skillset that contains the document
extraction skill and the text translation skill



Explanation:
The wiki contains text in English, French and Portuguese.
Scenario: All planned projects must support English, French, and Portuguese.
The Document Extraction skill extracts content from a file within the enrichment pipeline.
This allows you to take advantage of the document extraction step that normally happens
before the skillset execution with files that may be generated by other skills.
Note: The Translator Text API will be used to determine the from language. The Language
detection skill is not required.
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-document-extraction
https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-text-translation

You are developing the chatbot.
You create the following components:
• A QnA Maker resource
• A chatbot by using the Azure Bot Framework SDK
You need to add an additional component to meet the technical requirements and the
chatbot requirements. What should you add?


A.

Dispatch


B.

chatdown


C.

Language Understanding


D.

Microsoft Translator





A.
  

Dispatch



Explanation:
Scenario: All planned projects must support English, French, and Portuguese.
If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases),
you can use the Dispatch tool to determine which LUIS model or QnA Maker knowledge
base best matches the user input. The dispatch tool does this by creating a single LUIS
app to route user input to the correct model.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch

You are developing the chatbot.
You create the following components:
* A QnA Maker resource
* A chatbot by using the Azure Bot Framework SDK.
You need to integrate the components to meet the chatbot requirements.
Which property should you use?

 


A.

QnADialogResponseOptions.CardNoMatchText


B.

Qna MakerOptions-ScoreThreshold


C.

Qna Maker Op t ions StrickFilters


D.

QnaMakerOptions.RankerType





D.
  

QnaMakerOptions.RankerType



Explanation:
Scenario: When the response confidence score is low, ensure that the chatbot can provide
other response options to the customers.
When no good match is found by the ranker, the confidence score of 0.0 or "None" is
returned and the default response is "No good match found in the KB". You can override
this default response in the bot or application code calling the endpoint. Alternately, you
can also set the override response in Azure and this changes the default for all knowledge
bases deployed in a particular QnA Maker service.
Choosing Ranker type: By default, QnA Maker searches through questions and answers. If
you want to search through questions only, to generate an answer, use the
RankerType=QuestionOnly in the POST body of the GenerateAnswer request.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/bestpractices

You are developing the knowledgebase by using Azure Cognitive Search.
You need to meet the knowledgebase requirements for searching equivalent terms.
What should you include in the solution?


A.

synonym map


B.

a suggester


C.

a custom analyzer


D.

a built-in key phrase extraction skill





A.
  

synonym map



Explanation:
Within a search service, synonym maps are a global resource that associate equivalent
terms, expanding the scope of a query without the user having to actually provide the term.
For example, assuming "dog", "canine", and "puppy" are mapped synonyms, a query on
"canine" will match on a document containing "dog".
Create synonyms: A synonym map is an asset that can be created once and used by many
indexes.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-synonyms


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