Salesforce Certified AI Specialist Practice Test - Questions Answers, Page 10
List of questions
Question 91
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Universal Containers is evaluating Einstein Generative AI features to improve the productivity of the service center operation.
Which features should the AI Specialist recommend?
Explanation:
To improve the productivity of the service center, the AI Specialist should recommend the Service Replies and Case Summaries features.
Service Replies helps agents by automatically generating suggested responses to customer inquiries, reducing response time and improving efficiency.
Case Summaries provide a quick overview of case details, allowing agents to get up to speed faster on customer issues.
Work Summaries are not as relevant for direct customer service operations, and Sales Summaries are focused on sales processes, not service center productivity.
For more information, see Salesforce's Einstein Service Cloud documentation on the use of generative AI to assist customer service teams.
Question 92
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Amid their busy schedules, sales reps at Universal Containers dedicate time to follow up with prospects and existing clients via email regarding renewals or new deals. They spend many hours throughout the week reviewing past communications and details about their customers before performing their outreach.
Which standard Copilot action helps sales reps draft personalized emails to prospects by generating text based on previous successful communications?
Explanation:
For sales reps who need to draft personalized emails based on previous communications, the AI Specialist should recommend the Einstein Copilot Action: Draft or Revise Sales Email. This action uses AI to generate or revise email content, leveraging past successful communications to create personalized and relevant outreach to prospects or clients.
Find Similar Opportunities is used for opportunity matching, not email drafting.
Summarize Record provides a summary of customer data but does not directly help with drafting emails.
For more information, refer to Salesforce's Einstein Copilot documentation on standard actions for sales teams.
Question 93
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An AI Specialist wants to troubleshoot their Agent's performance.
Where should the AI Specialist go to access all user interactions with the Agent, including Agent erro|rs, incorrectly triggered actions, and incomplete plans?
Event Logs
Plan Canvas
Agent Settings
Explanation:
Event Logsin Salesforce capture detailed interaction data, including agent errors, triggered actions, and incomplete plans. These logs provide visibility into user-Agent interactions for troubleshooting performance issues. TheEinstein Bot Analyticsdocumentation highlights Event Logs as the primary source for auditing bot behavior and diagnosing issues like misconfigured actions or plan execution failures.
Plan Canvas(B) is for designing workflows, not auditing.
Agent Settings(C) control configuration but do not store interaction history.
Salesforce Help Article:Einstein Bot Analytics and Logs('Accessing Event Logs' section).
Einstein Bot Developer Guide: 'Monitoring and Debugging Agent Performance.'
Question 94
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What is an appropriate use case for leveraging Agentforce Sales Agent in a sales context?
Enable a sates team to use natural language to invoke defined sales tasks grounded in relevant data and be able to ensure company policies are applied. conversationally and in the now or work.
Enable a sales team by providing them with an interactive step-by-step guide based on business rules to ensure accurate data entry into Salesforce and help close deals fatter.
Instantly review and read incoming messages or emails that are then logged to the correct opportunity, contact, and account records to provide a full view of customer interactions and communications.
Explanation:
Agentforce Sales Agentis designed to let sales teams perform tasks vianatural language commands, leveraging Salesforce data while adhering to policies. For example, agents can ask the AI to 'update the opportunity stage to Closed Won' or 'generate a quote,' with the system enforcing validations and data security. This use case aligns with Salesforce's vision of conversational AI streamlining workflows without compromising compliance.
Step-by-step guides(B) are typically handled by tools like Dynamic Forms or Guided Selling, not Agentforce.
Logging messages/emails(C) is managed by Email-to-Case or Service Cloud, not a sales-specific AI agent.
Salesforce Help Article:Agentforce for Sales('Use Cases and Capabilities' section).
Einstein AI Specialist Trailhead: 'Sales Automation with Agentforce' (Natural Language Task Execution).
Question 95
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An AI specialist wants to leverage Record Snapshots grounding feature in a prompt template.
What preparations are required?
Configure page layout of the master record type
Create a field set for all the fields to be grounded
Enable and configure dynamic form for the object
Explanation:
To use theRecord Snapshots grounding featurein a prompt template, you mustcreate a field setthat includes all fields required for grounding. Field sets define which fields from an object are accessible to the AI model, ensuring the prompt template has structured data to generate contextually accurate responses. Salesforce documentation emphasizes that grounding relies on explicitly defined field sets to avoid exposing unintended data and to comply with security policies.
Page Layout configuration(A) controls UI organization but does not directly enable grounding.
Dynamic Forms(C) customize record pages dynamically but are unrelated to data grounding for prompts.
Salesforce Help Article:Grounding with Record Snapshots('Configuring Field Sets for Grounding' section).
Einstein GPT Implementation Guide: 'Preparing Data for AI Prompts' (Field Set requirements).
Question 96
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What is the importance of Action Instructions when creating a custom Agent action?
Action Instructions tell the user how to call this action in a conversation
Action Instructions tell the large language model (LLM) which action to use.
Action Instructions define the expected user experience of an action.
Explanation:
Action Instructionsare critical for defining how a custom Agent action should be executed, ensuring alignment with the intended user experience. They provide step-by-step guidance to the bot or LLM on logic, data handling, and integration workflows, directly impacting how users interact with the action. For example, clear instructions prevent errors in API calls or data processing, ensuring seamless interactions.
Salesforce documentation states that poorly defined instructions lead to mismatched expectations, while well-structured instructions ensure the action behaves predictably. This aligns with delivering a consistent user experience.
Arefers to user invocation, which is handled by dialogue flows, not instructions.
Bis incorrect because the LLM selects actions based on context/intent, not instructions.
Salesforce Help Article:Custom Action Configuration('Defining Action Instructions' section).
Einstein Bot Developer Guide: 'Designing User-Centric Actions with Instructions' (User Experience alignment).
Question 97
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Universal Containers (UC) wants to leverage Generative AI Salesforce functionality to reduce Service Agent handling time by providing recommended replies based on the existing Knowledge articles.
On which AI capability should UC train the service agents?
Case Replies
Knowledge Replies
Service Replies
Explanation:
Knowledge Repliesis the Einstein Generative AI capability that generates recommended responses for service agents by grounding responses in the organization'sKnowledge articles. This reduces handling time by providing contextually accurate suggestions sourced directly from approved content. Salesforce documentation states that Knowledge Replies leverage natural language processing (NLP) to match customer inquiries with relevant articles and draft replies, ensuring consistency and compliance.
Case Replies(A) focus on generating responses based on case data (e.g., case fields, history) but do not explicitly ground responses in Knowledge articles.
Service Replies(C) is not a standard Einstein capability tied to Knowledge-driven responses.
Salesforce Help Article:Einstein for Service -- Knowledge Replies('How Knowledge Replies Work' section).
Einstein for Service Implementation Guide: 'Reducing Handle Time with Knowledge-Driven Responses.'
Question 98
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Which part of the Einstein Trust Layer architecture leverages an organization's own data within a large language model (LLM) prompt to confidently return relevant and accurate responses?
Prompt Defense
Data Masking
Dynamic Grounding
Explanation:
Dynamic Groundingin the Einstein Trust Layer architecture ensures that large language model (LLM) prompts are enriched withorganization-specific data(e.g., Salesforce records, Knowledge articles) to generate accurate and relevant responses. By dynamically injecting contextual data into prompts, it reduces hallucinations and aligns outputs with trusted business data.
Prompt Defense(A) focuses on blocking malicious inputs or prompt injections but does not enhance responses with organizational data.
Data Masking(B) redacts sensitive information but does not contribute to grounding responses in business context.
Salesforce Help Article:Einstein Trust Layer -- Dynamic Grounding('How Dynamic Grounding Works' section).
Einstein Trust Layer Technical Overview: 'Contextual Accuracy with Dynamic Grounding.'
Question 99
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An AI Specialist at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the AI Specialist recommend to achieve this use case?
Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
Fine-Tune the standard foundational model due to the complexity of the data.
Deploy UC's own custom foundational model on this data first.
Explanation:
For no-code implementations,Prompt Builderallows AI Specialists to createprompt templatesthat dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
Salesforce Help Article:Prompt Builder for No-Code AI('Grounding in CRM and Data Cloud' section).
Einstein GPT Implementation Guide: 'Generating Summaries with Pre-Built Models.'
Question 100
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Universal Containers aims to streamline the sales team's daily tasks by using AI.
When considering these new workflows, which improvement requires the use of Prompt Builder?
Populate an Al-generated time-to close estimation to opportunities
Populate an AI generated summary field for sales contracts.
Populate an Al generated lead score for new leads.
Explanation:
Prompt Builderis explicitly required to createAI-generated summary fieldsvia prompt templates. These fields use natural language instructions to extract or synthesize information (e.g., summarizing contract terms). Time-to-close estimations (A) and lead scores (C) are typically handled by predictive AI (e.g., Einstein Opportunity Scoring) or analytics tools, which do not require Prompt Builder.
Salesforce Help Article:Create AI-Generated Fields with Prompt Builder('Summary Field Generation' example).
Einstein GPT for Sales Guide: 'Automating Contract Summaries.'
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