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100% Pass Quiz Salesforce-AI-Specialist - Newest Salesforce Certified AI Specialist Exam Test Question

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

Generative AI in CRM Applications: This part of the exam assesses AI specialists’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.

Topic 2

Einstein Trust Layer: This section evaluates the skills of Salesforce AI specialists responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.

Topic 3

Prompt Builder: This section evaluates the expertise of AI specialists working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.

Topic 4

Agentforce Tools: In this topic, AI specialists get knowledge using agents when it is appropriate. Moreover, the topic explains the working of agents and reasoning engine powers Agentforce. Lastly, the topic focuses on managing and monitoring agent adoption.

Topic 5

Model Builder: This portion of the exam focuses on Salesforce AI specialists' expertise in working with AI models within Salesforce environments. Candidates will need to demonstrate knowledge of when to use the Model Builder and how to configure standard, custom, or Bring Your Own Large Language Model (BYOLLM) generative models to meet business needs.

Salesforce Certified AI Specialist Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
Universal Containers wants to reduce overall agent handling time minimizing the time spent typing routine answers for common questionsin-chat, and reducing the post-chat analysis by suggesting values for case fields.
Which combination of Einstein for Service features enables this effort?

A. Einstein Reply Recommendations and Case Summaries
B. Einstein Reply Recommendations and Case Classification
C. Einstein Service Replies and Work Summaries

Answer: B

Explanation:
Universal Containers aims to reduce overall agent handling time by minimizing the time agents spend typing routine answers for common questions during chats and by reducing post-chat analysis through suggesting values for case fields.
To achieve these objectives, the combination ofEinstein Reply RecommendationsandCase Classificationis the most appropriate solution.
1. Einstein Reply Recommendations:
* Purpose:Helps agents respond faster during live chats by suggesting the best responses based on historical chat data and common customer inquiries.
* Functionality:
* Real-Time Suggestions:Provides agents with a list of recommended replies during a chat session, allowing them to quickly select the most appropriate response without typing it out manually.
* Customization:Administrators can configure and train the model to ensure the recommendations are relevant and accurate.
* Benefit:Significantly reduces the time agents spend typing routine answers, thus improving efficiency and reducing handling time.
2. Case Classification:
* Purpose:Automatically suggests or populates values for case fields based on historical data and patterns identified by AI.
* Functionality:
* Field Predictions:Predicts values for picklist fields, checkbox fields, and more when a new case is created.
* Automation:Can be set to auto-populate fields or provide suggestions for agents to approve.
* Benefit:Reduces the time agents spend on post-chat analysis and data entry by automating the classification and field population process.
Why Options A and B are Less Suitable:
* Option A (Einstein Service Replies and Work Summaries):
* Einstein Service Replies:Similar to Reply Recommendations but typically used for email and not live chat.
* Work Summaries:Provides summaries of customer interactions but does not assist in field value suggestions.
* Option B (Einstein Reply Recommendations and Case Summaries):
* Case Summaries:Generates a summary of the case details but does not help in suggesting field values.
References:
* Salesforce AI Specialist Documentation -Einstein Reply Recommendations:
* Details how Reply Recommendations assist agents in providing quick responses during live chats.
* Salesforce AI Specialist Documentation -Einstein Case Classification:
* Explains how Case Classification predicts and suggests field values to streamline case management.
* Salesforce Trailhead -Optimize Service with AI:
* Provides an overview of AI features that enhance service efficiency.

NEW QUESTION # 120
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?

A. Predict customer lifetime value of an account.
B. Predict most popular products from new product catalog.
C. Predict customer sentiment toward a promotion message.

Answer: A

Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.
Reference:
Salesforce Einstein Studio Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview

NEW QUESTION # 121
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process.
How should an AI Specialist apply the power of conversational AI to this use case?

A. Create a Flex prompt template in Prompt Builder.
B. Configure the Integration Flow Standard Action in Einstein Copilot.
C. Create a custom copilot action which calls a flow.

Answer: C

Explanation:
To enable Universal Containers service agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to create a custom copilot action that calls the flow. This action will allow Einstein Copilot to interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.
Option B is correct because it enables integration between Einstein Copilot and the flow that connects to Oracle ERP.
Option A (Flex prompt template) is more suited for static responses and not for invoking flows.
Option C (Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.
Reference:
Salesforce Einstein Copilot Actions: https://help.salesforce.com/s/articleView?id=einstein_copilot_actions.htm

NEW QUESTION # 122
Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to prompt templates.
Which type of flow should UC use?

A. Unified-object linking flow
B. Template-triggered prompt flow
C. Data Cloud-triggered flow

Answer: C

Explanation:
In this scenario,Universal Containerswants to bring data fromunified Data Cloud objectsinto prompt templates, and the best way to do that is through aData Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation onData Cloud-triggered flowsandData Cloud integrationswith generative AI solutions.

NEW QUESTION # 123
A support team handles a high volume of chat interactions and needs a solution to provide quick, relevant responses to customer inquiries.
Responses must be grounded in the organization's knowledge base to maintain consistency and accuracy.
Which feature in Einstein for Service should the support team use?

A. Einstein Knowledge Recommendations
B. Einstein Reply Recommendations
C. Einstein Service Replies

Answer: B

Explanation:
The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization's knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.
Einstein Service Replies (Option A) is focused on generating replies but doesn't have the same emphasis on grounding responses in the knowledge base.
Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.
Salesforce AI Specialist Reference:
For more information on Einstein Reply Recommendations: https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm

NEW QUESTION # 124
......

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