Salesforce SALESFORCE-AI-SPECIALIST Online Practice Questions and Exam Preparation

Salesforce SALESFORCE-AI-SPECIALIST Online Questions & Answers

• Question 21:

  Universal Containers plans to enhance its sales team's productivity using Al.

  Which specific requirement necessitates the use of Prompt Builder?

  A. Predicting the likelihood of customers churning or discontinuing their relationship with the company
  B. Creating a draft newsletter for an upcoming tradeshow
  C. Creating an estimated Customer Lifetime Value (CLV) with historical purchase data
  B. Creating a draft newsletter for an upcoming tradeshow

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  Explanation

• Question 22:

  Before activating a custom copilot action, an AI Specialist would like is to understand multiple real-world user utterances to ensure the action being selected appropriately.

  Which tool should the AI Specialist recommend?

  A. Model Playground
  B. Einstein Copilot
  C. Copilot Builder
  C. Copilot Builder

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  Explanation

  To understand multiple real-world user utterances and ensure the correct action is selected before activating a custom copilot action, the recommended tool is Copilot Builder. This tool allows AI Specialists to design and test conversational

  actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases. Copilot Builder provides the ability to test, refine, and improve actions based on real-world utterances.

  Option C is correct as Copilot Builder is designed for configuring and testing conversational actions.

  Option A (Model Playground) is used for testing models, not user utterances. Option B (Einstein Copilot) refers to the conversational interface but isn't the right tool for designing and testing actions.

  References:

  Salesforce Copilot Builder Overview:

  https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_builder.htm

• Question 23:

  Universal Containers is planning a marketing email about products that most closely match a customer's expressed interests.

  What should an AI Specialist recommend to generate this email?

  A. Standard email marketing template using Apex or flows for matching interest in products
  B. Custom sales email template which is grounded with interest and product information
  C. Standard email draft with Einstein and choose standard email template
  B. Custom sales email template which is grounded with interest and product information

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  Explanation

  To generate an email about products that closely match a customer's expressed interests, an AI Specialist should recommend using a custom sales email template that is grounded with interest and product information. This ensures that the

  email content is personalized based on the customer's preferences, increasing the relevance of the marketing message.

  Using grounding ensures that the generative AI pulls the correct data related to customer interests and product matches, making the email more effective. For more information, refer to Salesforce documentation on grounding AI-generated

  content and email personalization strategies.

• Question 24:

  Which mechanism within the Einstein Trust Layer helps to ensure that personal data is handled in compliance with data protection regulations like GDPR?

  A. Toxicity Scoring
  B. Data Masking
  C. Prompt Defense
  B. Data Masking

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  Explanation

• Question 25:

  Universal Containers' sales team engages in numerous video sales calls with prospects across the nation. Sales management wants an easy way to understand key information such as deal terms or customer sentiments. Which Einstein Generative AI feature should an AI Specialist recommend for this request?

  A. Einstein Call Summaries
  B. Einstein Conversation Insights
  C. Einstein Video KPI
  A. Einstein Call Summaries

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  Explanation

  Einstein Call Summaries is the best option for this scenario because it leverages Salesforce's AI capabilities to automatically summarize key details of video or voice calls. It includes details like deal terms, customer sentiments, follow-up

  tasks, and other crucial information. This feature is designed to help sales teams focus on their strategies rather than taking extensive manual notes during conversations. Einstein Call Summaries: Automatically generates summaries for calls,

  identifying critical points such as next steps and follow-ups, enhancing efficiency and understanding of deal progression.

  Einstein Conversation Insights: While it provides insights into customer sentiment and engagement, it is more suited for analyzing patterns across conversations rather than summarizing specific call details.

  Einstein Video KPI: Focuses on analyzing key performance indicators within video calls but does not offer summarization features needed for deal terms or sentiment tracking.

  This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.

  "Boost Sales with Automated AI Strategies | Salesforce Trailhead" .

  "Introduction to Einstein Discovery | Salesforce" .

• Question 26:

  How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?

  A. Masked data will be de-masked during response journey.
  B. Masked data will be de-masked during request journey.
  C. Responses that do not meet the relevance threshold will be automatically rejected.
  A. Masked data will be de-masked during response journey.

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  Explanation

  The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response

  journey.

  How It Works:

  Data Masking in the Request Journey:

  Processing by the LLM:

  De-masking in the Response Journey:

  Why Option A is Correct:

  De-masking During Response Journey: The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately. Balancing

  Security and Utility: This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.

  Why Options B and C are Incorrect:

  Option B (Masked data will be de-masked during request journey):

  Option C (Responses that do not meet the relevance threshold will be automatically rejected):

  References:

  Salesforce AI Specialist Documentation - Einstein Trust Layer Overview:

  Salesforce Help - Data Masking and De-masking Process:

  Salesforce AI Specialist Exam Guide - Security and Compliance in AI:

  Conclusion:

  The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful

  responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.

• Question 27:

  An administrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM data. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt.

  Which feature in the Einstein Trust Layer best supports UC's need?

  A. Data masking
  B. Dynamic grounding with secure data retrieval
  C. Zero-data retention policy
  B. Dynamic grounding with secure data retrieval

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  Explanation

  Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI- generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data. Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model's outputs are trustworthy and reliable for business use. The other options are less aligned with the requirement: Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts. Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.

  References: Salesforce Developer Documentation on Einstein Trust Layer Salesforce Security Documentation for AI and Data Privacy

• Question 28:

  An AI Specialist is tasked to optimize a business process flow by assigning actions to agents within the Salesforce Agentforce Platform.

  What is the correct method for the AI Specialist to assign actions to an Agent?

  A. Assign the action to a Topic First in Agent Builder.
  B. Assign the action to a Topic first on the Agent Actions detail page.
  C. Assign the action to a Topic first on Action Builder.
  C. Assign the action to a Topic first on Action Builder.

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  Explanation

  Action Builder is the central place in Salesforce Agentforce where you define and manage actions that your AI agents can perform. This includes connecting actions to various tools and systems.

  Topics in Agentforce represent the different tasks or intents that an AI agent can handle. By assigning an action to a Topic in Action Builder, you're essentially telling the agent, "When you encounter this type of request or situation, perform

  this action."

• Question 29:

  Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.

  No data is used for LLM training or product improvements by third- party LLMs.

  No data is retained outside of UC's Salesforce org.

  The data sent cannot be accessed by the LLM provider.

  Which property of the Einstein Trust Layer should the AI Specialist highlight to UC that addresses these requirements?

  A. Prompt Defense
  B. Zero-Data Retention Policy
  C. Data Masking
  B. Zero-Data Retention Policy

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  Explanation

  Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data. The Zero-Data Retention Policy in the Einstein

  Trust Layer is designed to address these concerns by ensuring that:

  No data is used for training or product improvements by third-party LLMs. No data is retained outside of the customer's Salesforce organization.

  The LLM provider cannot access any customer data.

  This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.

  Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.

  References:

  Salesforce Einstein Trust Layer Documentation:

  https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm

• Question 30:

  Universal Containers wants to utilize Einstein for Sales to help sales reps reach their sales quotas by providing Al-generated plans containing guidance and steps for closing deals.

  Which feature should the AI Specialist recommend to the sales team?

  A. Find Similar Deals
  B. Create Account Plan
  C. Create Close Plan
  C. Create Close Plan

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  Explanation

  The "Create Close Plan" feature is designed to help sales reps by providing AI-generated strategies and steps specifically focused on closing deals. This feature leverages AI to analyze the current state of opportunities and generate a plan

  that outlines the actions, timelines, and key steps required to move deals toward closure. It aligns directly with the sales team's need to meet quotas by offering actionable insights and structured plans.

  Find Similar Deals (Option A) helps sales reps discover opportunities similar to their current deals but doesn't offer a plan for closing. Create Account Plan (Option B) focuses on long-term strategies for managing accounts, which might

  include customer engagement and retention, but doesn't focus on deal closure.

  Salesforce AI Specialist

  References:For more information on using AI for sales, visit:

  https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm