1z0-1071-23 Sample Questions Answers

Integrating custom logic in the dialog flow

Integrating live agent transfer

Authenticating users with Auth2 credentials

Integrating a Webview app in the dialog flow

Is natural, collaborative, and personable

Acknowledges input and shows that It understands it

Sometimes makes lighthearted digressions

Embraces positive human qualities like empathy, humor, and contrition (when appropriate)

A resource bundle populated with phrases such as "check balance," "What ls my cur-rent balance?" and " How much money do I have"

Dialog flows with an Answer QUESTION NO: component to read user input

An intent that has been trained with utterances such as "check balance," "What Is my current balance?" and "How much money do I have"

An input form rendered by a webview that Is hosted within a digital assistant

An entity that defines account types with values such as "checking" or "savings"

Establishing quantitative measures of NLP Improvements to determine the success of the model

Working closely With the integration team to ensure that the skill can make calls to the backend as needed.

Implementing conversation flows that include entity extraction, varied prompting, validation, error handling, and disambiguation

Setting up data manufacturing jobs with high-quality guidance and questions

Defining processes related to versioning and deployment, ensuring that changes are property managed and rolled out without disrupting the user experience

It refers to machines that mimic human Intelligence to perform tasks and iteratively Improve themselves based on the Information they collect.

It is a subset of artificial intelligence that focuses on a conversational interaction with a human.

It is the trademarked name of the APIs behind ChatGPT.

It is a multipurpose Chabot that can converse with users on a variety of topics.

You should have no more than 10 Intents in each skill.

Each intent should have a roughly similar number of utterances.

The model you train each intent on depends on the number of utterances that you have.

You should include spelling mistakes and ungrammatical phrases in about half of the utterances.

Build flows that focus on a single conversational task.

limit the number of flows per skill.

Delegate reusable functionality to service flows.

Keep flows small and simple.

They define a business domain object as a collection of related system and custom entitles.

When you add entitles to the composite bag, you can control how they get extracted with respect to other entities and when they are prompted for.

You need to create a separate composite bag to address nonentity types, such as strings, locations, and attachments.

The composite bag can resolve all entity values using only a single state in the dialog flow.

The composite bag always enforces every entity Item to have a valid value before allowing the conversation to move to the next state in the dialog flow.

Create a full set of training data in each language, where the utterances for each language are provided by native speakers of that language.

Create a full set of training data in English, test the model in all three languages, and then incrementally add unique training data in the other skills until you get the quality or intent resolution that you are aiming for.

Create a full set of training data in English and then have that training data translated into the other two languages and add it to the skill as well.

Create a full set of training data in English without adding any training data in the other languages.

Ensures the user always gets the answer they are seeking

Guides the user through the turns and expectations of a conversation

Makes the conversation engaging to the user

Works to reduce ambiguity to avoid misunderstandings

Defining the use cases, goals, and measures of success for the project

Ensuring viability of the NLP model, including designing Intents

Designing the details of each specific flow and how these can be reused during development

Being the designated expert on all things related to conversational AI

It can communicate with customers through different messaging platforms.

It intelligently routes user messages based on NLP and rule-based routing.

It ensures that a user completes a conversation in skill before allowing that user to trigger the intent of a different skill.

It can be used to manage a set of skills, including skills that you create yourself and skills that you have added from Skill Store.

To capture all possible Input

To prevent the user from getting permanently stuck on a given QUESTION NO: or request

To eliminate the need for error handling

To provide automated error messages