Topic 2: Contoso, Ltd.Case Study
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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
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
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
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
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
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
You build a QnA Maker resource to meet the chatbot requirements.
Which RBAC role should you assign to each group? To answer, select the appropriate
options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Cognitive Service User
Ensure that the members of a group named Management-Accountants can approve the
FAQs.
Approve=publish.
Cognitive Service User (read/write/publish): API permissions: All access to Cognitive
Services resource except for ability to:
1. Add new members to roles.
2. Create new resources.
Box 2: Cognitive Services QnA Maker Editor
Ensure that the members of a group named Consultant-Accountants can create and
amend the FAQs.
QnA Maker Editor: API permissions:
1. Create KB API
2. Update KB API
3. Replace KB API
4. Replace Alterations
5. "Train API" [in new service model v5]
Box 3: Cognitive Services QnA Maker Read
Ensure that the members of a group named the Agent-CustomerServices can browse the
FAQs.
QnA Maker Read: API Permissions:
1. Download KB API
2. List KBs for user API
3. Get Knowledge base details
4. Download Alterations
Generate Answer
You build a Language Understanding model by using the Language Understanding portal.
You export the model as a JSON file as shown in the following sample.
To what does the Weather.Historic entity correspond in the utterance?
A.
by month
B.
chicago
C.
rain
D.
location
by month
You have a library that contains thousands of images.
You need to tag the images as photographs, drawings, or clipart.
Which service endpoint and response property should you use? To answer, select the
appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
You are building a model that will be used in an iOS app.
You have images of cats and dogs. Each image contains either a cat or a dog.
You need to use the Custom Vision service to detect whether the images is of a cat or a
dog.
How should you configure the project in the Custom Vision portal? To answer, select the
appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Graphical user
interface, text, application, email
Description automatically generated
Box 1: Classification
Box 2: Multiclass
A multiclass classification project is for classifying images into a set of tags, or target
labels. An image can be assigned to one tag only.
Box 3: General
General: Optimized for a broad range of image classification tasks. If none of the other
specific domains are appropriate, or if you're unsure of which domain to choose, select one
of the General domains.
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