Salesforce - Priscilla Hong

Question 1:

Universal Containers has a new AI project. What should an Agentforce consider when adding a related list on the Account object to be used in the prompt template?

A.    After selecting a related list from the Account, use the field picker to choose merge fields in Prompt Builder.

B.    Prompt Builder must be used to assign the fields from the related list in a JSON format.

C.    The fields for the related list are based on the default page layout of the Account for the current user.

Correct Answer: A

Explanation: The recommended approach in Salesforce's documentation is to select a related list and then use the field picker to add the necessary fields to your AI prompt. This ensures the prompt has exactly the data you need from that related list.

- Why Not Option B (JSON Formatting): No Mandatory JSON Requirement: Although you can structure data as JSON if you desire advanced formatting, Prompt Builder does not require you to manually assign the fields from the related list in JSON. The platform automatically handles how the data is passed along in the background.

- Why Not Option C (Default Page Layout): Independent of Page Layout: Prompt Builder does not rely strictly on the default page layout for fields. You can configure the fields you want from the related list, independent of how the user's page layout is set up in the UI.

Question 2:

Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature. What does Einstein consider when generating a summary?

A.    Generation is grounded with conversation context, Knowledge articles, and cases.

B.    Generation is grounded with existing conversation context only.

C.    Generation is grounded with conversation context and Knowledge articles.

Correct Answer: A

Explanation: When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.

- Conversation Context: Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.

- Knowledge Articles: It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.

- Cases: Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.

Question 3:

Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization. What should the Agentforce Specialist recommend?

A.    Agent Analytics

B.    Agentforce Analytics

C.    Agent Studio Analytics

Correct Answer: A

Explanation: Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.

* Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.

* Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.

Question 4:

When creating a custom retriever in Einstein Studio, which step is considered essential?

A.    Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.

B.    Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.

C.    Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space, and the model, and then define the ranking method.

Correct Answer: A

Explanation: In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:

* Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.

* Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.

* Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data. Filters are noted as optional in Option A, which is accurate - they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.

* Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A) before output can be configured.

* Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.

Question 5: 

What is the primary function of the planner service in the Einstein Copilot system?

A.    Generating record queries based on conversation history

B.    Offering real-time language translation during conversations

C.    Identifying copilot actions to respond to user utterances

Correct Answer: C

Explanation:

The primary function of the planner service in the Einstein Copilot system is to identify copilot Actions that should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Einstein Copilot should perform based on user input.

Question 6: 

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 a reply to a request for a proposal via a personalized email.

Correct Answer: C

Explanation:

* 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. Sending a Personalized RFP Email (Option C) is a classic example of using generative AI to compose well-structured, context-aware text.

* 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.

Question 7: 

A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers.

Which solution approach should the admin leverage?

A.    Use sales Email standard templates

B.    Create a t field Generation prompt template type

C.    Create a Sales Email prompt template type.

Correct Answer: C

Explanation:

To generate personalized emails using LLMs while reusing templates: Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.

* Option A: Standard email templates lack LLM integration and dynamic personalization.

* Option B: "t field Generation" is not a valid Salesforce prompt template type.

Question 8: 

An Agentforce is creating a custom action in Einstein Copilot. Which option is available for the Agentforce Specialist to choose for the custom copilot action?

A.    Apex trigger

B.    SOQL

C.    Flows

Correct Answer: C

Explanation:

When creating a custom action in Einstein Copilot, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code. While Apex triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Einstein Copilot because they are declarative and highly adaptable.

Question 9: 

An Agentforce needs to enable the use of Sales Email prompt templates for the sales team. The Agentforce Specialist has already created the templates in Prompt Builder. According to best practices, which steps should the Agentforce Specialist take to ensure the sales team can use these templates?

A.    Assign the Prompt Template User permission set and enable Sales Emails in Setup.

B.    Assign the Prompt Template Manager permission set and enable Sales Emails in setup.

C.    Assign the Data Cloud Admin permission set and enable Sales Emails in Setup.

Correct Answer: A

Explanation: To enable Sales Email prompt templates:

* Permission Set: Assign the Prompt Template User permission set to the sales team to grant access to use pre-built templates.

* Feature Activation: Enable Sales Emails in Salesforce Setup to activate the integration between prompt templates and email workflows.

* Option B (Manager permission set): Required for creating/modifying templates, not for usage.

* Option C (Data Cloud Admin): Unrelated to prompt template access.

Question 10: 

Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases. UC is considering implementing Einstein Copilot and extending Service Cloud to mobile users. When would Einstein Copilot implementation be most advantageous?

A.    When the goal is to streamline customer support processes and improve response times

B.    When the main objective is to enhance data security and compliance measures

C.    When the focus is on optimizing marketing campaigns and strategies

Correct Answer: A

Explanation: Einstein Copilot implementation would be most advantageous in Salesforce Service Cloud when the goal is to streamline customer support processes and improve response times. Einstein Copilot can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.

* Option B (data security)is not the primary focus of Einstein Copilot, which is more about improving operational efficiency.

* Option C (marketing campaigns)falls outside the scope of Service Cloud and Einstein Copilot's primary benefits, which are aimed at improving customer service and case management.