Free Access Oracle.1Z0-1071-21.v2022-08-18.q28 Practice Test (Page 3)

Question 6

Which two components can be used in combination with composite bag entities to auto-generate skill responsesand flows from definitions saved in bag items?

A.System.ResolveEntities

B.System.Text

C.System.MatchEntity

D.System.CommonResponse

E.System.List

Question 7

Which two features of Oracle Digital Assistant should you use to allow a skill to specifically classify the user message "What's my balance in savings?" and to identify the type of account?

A.dialog flows with a system.Text component to read the user input

B.an input form rendered by a webview that is hosted within a Digital Assistant

C.an entity that defines account types (with values such as "checking" or "savings")

D.an intent that is been trained with utterances such as "check balance", "What is my current balance?", and "How much money do I have?"

E.a resource bundle populated with phrases such as "check balance", "What is my current balance?", and
"How much money do I have?"

Question 8

Which two statements about skills are true?

A.Skills always use natural languages processing (NLP).

B.Customers can only chat with skill when those skills managed by a digital assistant.

C.Skills can access back-end services.

D.Skills have dialog flows that you may configure to create conversation.

Question 9

intent has been configured with a composite bag entity.
Which statement is FALSE?

A.The skill may allow users to update their previous input.

B.The conversation is entirely sequential, where users can only input values in the order determined by the dialog flow definition.

C.The composite bag entity slots values as they are provided from the user input. It then prompts for other entityitem values.

D.The composite bag entity is typically resolved using a system.ResolveEntities component or a system.commonResponse component.

Question 10

What is the purpose of the training models within Oracle Digital Assistant?

A.automatically create n number of classifications where n is a parameter defined for each skill

B.automatically crowdsource sample data towhich user input is matched

C.build a complete semantic language model allowing a skill to understand 98% of user input in the trained language

D.allow a skill to classify user input to defined intents

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