Salesforce Certified AI Specialist Practice Test - Questions Answers, Page 10

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.

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.'

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).

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).

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).

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.'

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.'

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.'

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.'