An AI Specialist is tasked with analyzing Agent interactions looking into user inputs, requests, and queries to identify patterns and trends.
What functionality allows the AX Specialist to achieve this?
A. User Utterances dashboard B. Agent Event Logs dashboard C. AI Audit & Feedback Data dashboard
Answer: A Explanation The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the AI Specialist to: Identify Common Queries: Surface frequently asked questions or recurring issues.
Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.
Why Other Options Are Incorrect:
B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.
C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.
References:
Salesforce Einstein AI Specialist Certification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.
Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.
Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.
Question # 42
An Al Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?
A. Action Input B. Action Name C. Action Instructions
Answer: C Explanation To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus on Action Instructions. Here's why: Action Instructions define the logic, parameters, and steps the AI should follow to execute the action. They include: How input data is processed. API calls or Apex invocations. Conditional logic (e.g., decision trees).Testing and iterating on these instructions ensures alignment with the intended workflow. For example, incorrect API endpoint references or misconfigured parameters in the instructions will cause failures. Action Input (Option A) refers to the data provided to the action. While validating input formats is important, inputs are static once defined. The primary issue lies in whether the instructions correctly use the inputs. Action Name (Option B) is a descriptive label and does not affect functionality. Salesforce Documentation Support: Salesforce Einstein Bots & Custom Actions Guide highlights that Action Instructions are where the "core logic" resides, requiring rigorous testing (Source: Einstein Bots Developer Guide). Trailhead Module "Build Custom Actions for Einstein Bots" emphasizes refining instructions to handle edge cases and validate outputs (Source: Trailhead). By iterating on Action Instructions, the AI Specialist ensures the action’s logic, integrations, and error handling are robust.
Question # 43
Universal Containers, dealing with a high volume of chat inquiries, implements Einstein Work Summaries to boost productivity.
After an agent-customer conversation, which additional information does Einstein generate and fill, apart from the "summary"'
A. Sentiment Analysis and Emotion Detection B. Draft Survey Request Email C. Issue and Revolution
Answer: C
Explanation Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and supervisors quickly grasp the conversation's context without reviewing the full transcript. Sentiment Analysis and Emotion Detection (Option A): While Einstein Conversation Insights provides sentiment scores and emotion detection, these are separate from Work Summaries. Work Summaries focus on factual summaries, not sentiment. Draft Survey Request Email (Option B): Not part of Work Summaries. This would require automation tools like Flow or Email Studio. Issue and Resolution (Option C): Directly referenced in Salesforce documentation as fields populated by Einstein Work Summaries. References: Salesforce Help Article: Einstein Work Summaries Einstein Work Summaries focus on "key details like Issue and Resolution" alongside summaries. Contrast with Einstein Conversation Insights for sentiment/emotion analysis.
Question # 44
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities.
How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?
A. The LLM interprets the user's request, generates a plan by identifying the apcMopnete topics and actions, and executes the actions to retrieve and display the open opportunities B. The LLM uses a static set of rules to match the user's request with predefined topics and actions, bypassing the need for dynamic interpretation and planning. C. Using a dialog pattern. the LLM matches the user query to the available topic, action and steps then performs the steps for each action, such as retrieving a fast of open opportunities.
Answer: A Explanation Agentforce’s LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner’s role in converting intents into actionable steps while adhering to security and business logic.
Question # 45
Which business requirement presents a good use case for leveraging Einstein Prompt Builder?
A. Forecast future sales trends based on historical data. B. Identify potential high-value leads for targeted marketing campaigns. C. Send reply to a request for proposal via a personalized email.
Answer: C Explanation Context of the Question Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models. The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder. Einstein Prompt Builder Typical Use Cases
Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.
Why Not Forecast Future Sales Trends or Identify Potential High-Value Leads?
(Option A) Forecasting trends typically involves predictive analytics and modeling capabilities found in Einstein Discovery or standard reporting, not generative text solutions.
(Option B) Identifying leads for marketing campaigns involves lead scoring or analytics, again an Einstein Discovery or Lead Scoring scenario.
Sending a Personalized RFP Email(Option C) is a classic example of using generative AI to compose well-structured, context-aware text.
ConclusionOption C(Send reply to a request for proposal via a personalized email) is the best match for Einstein Prompt Builder’s generative text functionality.
Salesforce AI Specialist References & Documents
Salesforce Documentation:Einstein Prompt Builder OverviewHighlights how to use Prompt Builder to create and customize text-based responses, especially for email or record fields.
Salesforce AI Specialist Study GuideExplains that generative AI features in Salesforce are designed for creating or summarizing text, not for advanced predictive use cases (like forecasting or lead scoring).
Question # 46
Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?
A. Case Summaries B. Service Replies C. Case Escalation
Answer: B
Explanation
Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.
Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.
Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.
Thus,Service Repliesis the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.
Question # 47
Universal
Containers wants to incorporate the current order fulfillment status into a
prompt for a large language model (LLM).
The order status
is stored in the external
enterprise resource planning
(ERP) system.
Which data grounding technique
should the AI Specialist recommend?
A. Eternal Object Record Merge Fields B. External Services Merge Fields C. Apex Merge Fields
Answer: A Context of the Requirement:Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
Data Grounding in LLM Prompts:Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to useExternal Objects(via Salesforce Connect) when data resides outside of Salesforce.
Why External Object Record Merge Fields:
External Objectsappear much like standard or custom objects but map to tables in external systems.
You can reference fields from these External Objects in merge fields, allowing real-time data retrieval from the external ERP system without storing that data natively in Salesforce.
This is a simpler “point-and-reference” approach compared to coding custom Apex or configuring external services for direct prompt embedding.
Why Not External Services Merge Fields or Apex Merge Fields:
External Services Merge Fieldstypically leverage flows or external service definitions. While feasible, it is more about orchestrating or invoking external services for automation (e.g., Flow). It’s not the standard approach for seamlessly referencing external recorddata in prompt merges.
Apex Merge Fieldswould imply custom Apex code controlling the prompt insertion. While possible, it’s less “clicks not code” friendly and is not the default method for referencing typical record data.
References and Study Resources:
Salesforce Help & Training#Salesforce Connect and External Objects
Salesforce Trailhead#“Integrate External Data with Salesforce Connect”
Salesforce AI Specialist Study Resources(documentation regarding how to ground LLM prompts using External Objects)
Question # 48
Universal Container's internal auditing team asks an AI Specialist to verify that address information is
properly masked in the prompt being generated. How should the AI Specialist verify the privacy
of the masked data in the Einstein
Trust Layer?
A. Enable data encryption on the address field B. Review the platform event logs C. Inspect the AI audit trail
Answer: C Explanation TheAI audit trailin Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the AI Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs. Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations. Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes. Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities.
Question # 49
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?
A. Prompt Defense B. Data Masking C. Dynamic Grounding
Answer: C
Explanation
Dynamic Grounding in the Einstein Trust Layer architecture ensures that large language model (LLM) prompts are enriched with organization-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.
Question # 50
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?
A. Service AI Grounding only works with Case and Knowledge objects. B. Service AI Grounding only supports String and Text Area type fields. C. Service AI Grounding visibility works m system mode.
Answer: B
Explanation
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses. Key considerations: Field Types: Standard Service AI Grounding supports String and Text Area fields. Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct. Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object. Visibility: Service AI Grounding respects user permissions and sharing settings unless overridden (Option C is incorrect). References: Salesforce Help: Service AI Grounding Requirements Explicitly states support for "Text Area and String fields" only.