Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions. Which use case functionality of Model Builder aligns with UC's request?
A. Product recommendation prediction
B. Customer churn prediction
C. Contract Renewal Date prediction
Explanation
Customer churn prediction is the best use case for Model Builder in addressing Universal Containers'
concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn, UC can proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
Option B is correct because customer churn prediction aligns with UC's need to reduce cancellations through proactive measures.
Option A (product recommendation prediction) is unrelated to contract cancellations.
Option C(contract renewal date prediction) addresses timing but does not focus on predicting potential cancellations.
Universal Containers (UC) has recently received an increased number of support cases. As a result, UC has hired more customer support reps and has started to assign some of the ongoing cases to newer reps.
Which generative AI solution should the new support reps use to understand the details of a case without reading through each case comment?
A. Einstein Copilot
B. Einstein Sales Summaries
C. Einstein Work Summaries
Explanation
New customer support reps at Universal Containers can use Einstein Work Summaries to quickly understand the details of a case without reading through each case comment. Work Summaries leverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.
Einstein Copilot can assist with a variety of tasks but is not specifically designed for summarizing case details.
Einstein Sales Summaries are focused on summarizing sales-related activities, which is not applicable for support cases.
What is automatically created when a custom search index is created in Data Cloud?
A. A retriever that shares the name of the custom search index.
B. A dynamic retriever to allow runtime selection of retriever parameters without manual configuration.
C. A predefined Apex retriever class that can be edited by a developer to meet specific needs.
Explanation
Comprehensive and Detailed In-Depth Explanation: In Salesforce Data Cloud, a custom search index is created to enable efficient retrieval of data (e.g., documents, records) for AI-driven processes, such as grounding Agentforce responses. Let’s evaluate the options based on Data Cloud’s functionality.
Option A: A retriever that shares the name of the custom search index. When a custom search index is created in Data Cloud, a corresponding retriever is automatically generated with the same name as the index. This retriever leverages the index to perform contextual searches (e.g., vector-based lookups) and fetch relevant data for AI applications, such as Agentforce prompt templates. The retriever is tied to the indexed data and is ready to use without additional configuration, aligning with Data Cloud’s streamlined approach to AI integration. This is explicitly documented in Salesforce resources and is the correct answer.
Option B: A dynamic retriever to allow runtime selection of retriever parameters without manual configuration. While dynamic behavior sounds appealing, there’s no concept of a "dynamic retriever" in Data Cloud that adjusts parameters at runtime without configuration. Retrievers are tied to specific indexes and operate based on predefined settings established during index creation. This option is not supported by official documentation and is incorrect.
Option C: A predefined Apex retriever class that can be edited by a developer to meet specific needs. Data Cloud does not generate Apex classes for retrievers. Retrievers are managed within the Data Cloud platform as part of its native AI retrieval system, not as customizable Apex code. While developers can extend functionality via Apex for other purposes, this is not an automatic outcome of creating a search index, making this option incorrect.
Why Option A is Correct: The automatic creation of a retriever named after the custom search index is a core feature of Data Cloud’s search and retrieval system. It ensures seamless integration with AI tools like Agentforce by providing a ready-to-use mechanism for data retrieval, as confirmed in official documentation.
Universal Container (UC) has effectively utilized prompt templates to update summary fields on Lightning record pages. An admin now wishes to incorporate similar functionality into UC's automation process using Flow.
How can the admin get a response from this prompt template from within a flow to use as part of UC's automation?
A. Invocable Apex
B. Flow Action
C. Einstein for Flow
Where should the Agentforce Specialist go to add/update actions assigned to a copilot?
A. Copilot Actions page, the record page for the copilot action, or the Copilot Action Library tab
B. Copilot Actions page or Global Actions
C. Copilot Detail page, Global Actions, or the record page for the copilot action
Explanation
To add or update actions assigned to a copilot, An Agentforce can manage this through several areas:
Copilot Actions Page: This is the central location where copilot actions are managed and configured.
Record Page for the Copilot Action: From the record page, individual copilot actions can be updated or modified.
Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for Copilot can be accessed and modified.
These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring that the AI assistant remains aligned with business requirements and processes.
The other options are incorrect:
B misses the Copilot Action Library, which is crucial for managing actions.
C includes the Copilot Detail page, which isn't the primary place for action management.
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?
A. Populate an Al-generated time-to close estimation to opportunities
B. Populate an AI generated summary field for sales contracts.
C. Populate an Al generated lead score for new leads.
Explanation
Prompt Builder is explicitly required to create AI-generated summary fields via 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.
Universal Container's internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated.
How should the Agentforce 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
Explanation
The AI 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 Agentforce 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.
Before activating a custom copilot action, An Agentforce would like is to understand multiple real-world user utterances to ensure the action being selected appropriately.
Which tool should the Agentforce Specialist recommend?
A. Model Playground
B. Einstein Copilot
C. Copilot Builder
Universal Containers has seen a high adoption rate of a new feature that uses generative AI to populate a summary field of a custom object, Competitor Analysis. All sales users have the same profile but one user cannot see the generative AlI-enabled field icon next to the summary
field.
What is the most likely cause of the issue?
A. The user does not have the Prompt Template User permission set assigned.
B. The prompt template associated with summary field is not activated for that user.
C. The user does not have the field Generative AI User permission set assigned.
Explanation
In Salesforce, Generative AI capabilities are controlled by specific permission sets. To use features such as generating summaries with AI, users need to have the correct permission sets that allow access to these functionalities.
Generative AI User Permission Set: This is a key permission set required to enable the generative AI capabilities for a user. In this case, the missing Generative AI User permission set prevents the user from seeing the generative AI-enabled field icon. Without this permission, the generative AI feature in the Competitor Analysis custom object won't be accessible.
Why not A? The Prompt Template User permission set relates specifically to users who need access to prompt templates for interacting with Einstein GPT, but it's not directly related to the visibility of AI- enabled field icons.
Why not B? While a prompt template might need to be activated, this is not the primary issue here. The question states that other users with the same profile can see the icon, so the problem is more likely to be permissions-based for this particular user.
For more detailed information, you can review Salesforce documentation on permission sets related to AI capabilities at Salesforce AI Documentation and Einstein GPT permissioning guidelines.
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 model, then define the ranking method.
Explanation
Comprehensive and Detailed In-Depth 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.
Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
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