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Topic 3: Misc. Questions
You are building a multilingual chatbot.
You need to send a different answer for positive and negative messages.
Which two Text Analytics APIs should you use? Each correct answer presents part of the
solution. (Choose two.)
NOTE: Each correct selection is worth one point.
A.
Linked entities from a well-known knowledge base
B.
Sentiment Analysis
C.
Key Phrases
D.
Detect Language
E.
Named Entity Recognition
Sentiment Analysis
Detect Language
Explanation:
B: The Text Analytics API's Sentiment Analysis feature provides two ways for detecting
positive and negative sentiment. If you send a Sentiment Analysis request, the API will
return sentiment labels (such as "negative", "neutral" and "positive") and confidence scores
at the sentence and document-level.
D: The Language Detection feature of the Azure Text Analytics REST API evaluates text
input for each document and returns language identifiers with a score that indicates the
strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is
unknown. Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/textanalytics-
how-to- sentiment-analysis?tabs=version-3-1
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/textanalytics-
how-to- language-detection
Match the Azure Cosmos DB APIs to the appropriate data structures.
To answer, drag the appropriate API from the column on the left to its data structure on the
right. Each API may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
You are building an app that will enable users to upload images. The solution must meet
the following requirements:
• Automatically suggest alt text for the images.
• Detect inappropriate images and block them.
• Minimize development effort.
You need to recommend a computer vision endpoint for each requirement.
What should you recommend? To answer, select the appropriate options in the answer
area.
NOTE: Each correct selection is worth one point.
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to
[email@domain.com]
You need to select the entity types. The solution must use built-in entity types to minimize
training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity
types to the correct labels. Each entity type may be used once, more than once, or not at
all.
You may need to drag the split bar between panes or scroll to view content.
Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you
do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from
an utterance. Because this entity is already trained, you do not need to add example
utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
You develop a custom question answering project in Azure Cognitive Service for
Language. The project will be used by a chatbot. You need to configure the project to
engage in multi-turn conversations. What should you do?
A.
Add follow-up prompts
B.
Enable active learning.
C.
Add alternate questions
D.
Enable chit-chat
Enable chit-chat
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