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Free AI-102 Exam Braindumps (page: 35)

Page 34 of 83
PDF with 329 Questions

DRAG DROP (Drag and Drop is not supported)
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.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


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.


Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2 https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity



You have the following C# method.
You need to deploy an Azure resource to the East US Azure region. The resource will be used to perform sentiment analysis.
How should you call the method?

  1. create_resource("res1", "ContentModerator", "S0", "eastus")
  2. create_resource("res1", "TextAnalytics", "S0", "eastus")
  3. create_resource("res1", "ContentModerator", "Standard", "East US")
  4. create_resource("res1", "TextAnalytics", "Standard", "East US")

Answer(s): B

Explanation:

To perform sentiment analysis, we specify TextAnalytics, not ContentModerator.
Possible SKU names include: 'F0','F1','S0','S1','S2','S3','S4','S5','S6','S7','S8'
Possible location names include: westus, eastus


Reference:

https://docs.microsoft.com/en-us/powershell/module/az.cognitiveservices/new-azcognitiveservicesaccount



You build a Conversational Language Understanding model by using the Language Services 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?

  1. by month
  2. chicago
  3. rain
  4. location

Answer(s): A



You are examining the Text Analytics output of an application.
The text analyzed is: `Our tour guide took us up the Space Needle during our trip to Seattle last week.`
The response contains the data shown in the following table.
Which Text Analytics API is used to analyze the text?

  1. Entity Linking
  2. Named Entity Recognition
  3. Sentiment Analysis
  4. Key Phrase Extraction

Answer(s): B

Explanation:

Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.


Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/named-entity-recognition/overview






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