Salesforce Agentforce-Specialist Online Access

Comprehensive and Detailed In-Depth Explanation:

UC has grounded a prompt template with a related list, but the responses are incorrect during UAT. Grounding with related lists in Agentforce allows the AI to access data from child records linked to a parent object. Let’s analyze the options.

Option A: The related list is Read Only.Read-only status (e.g., via field-level security or sharing rules) might limit user edits, but it doesn’t inherently prevent the AI from accessing related list data for grounding, as long as the running user (or system context) has read access. This is unlikely to cause incorrect responses and is not a primary consideration, making it incorrect.

Option B: The related list prompt template option is not enabled.There’s no specific "related list prompt template option" toggle in Prompt Builder. When grounding with a Record Snapshot or Flex template, related lists are included if properly configured (e.g., via object relationships). This option seems to be a misphrasing and doesn’t align with documented settings, making it incorrect.

Option C: The related list is not on the parent object’s page layout.In Agentforce, grounding with related lists relies on the related list being defined and accessible in the parent object’s metadata, often tied to its presence on the page layout. If the related list isn’t on the layout, the AI might not recognize or retrieve its data correctly, leading to incomplete or incorrect responses. Salesforce documentation notes that related list data availability can depend on layout configuration, making this a plausible and common issue during UAT, and thus the correct answer.

Why Option C is Correct:

The absence of the related list from the parent object’s page layout can disrupt data retrieval for grounding, leading to incorrect AI responses. This is a known configuration consideration in Agentforce setup and testing, as per official guidance.

Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.

Option A: Adjusting model temperature is a parameter-level setting for controlling randomness in AI-generated responses but does not necessitate connecting to an external foundational model.

Option B: This is the correct answer because Einstein Studio supports fine-tuning external models with proprietary company data, enabling a tailored and more accurate AI solution for UC.

Option C: Changing frequency penalties is another parameter-level adjustment and does not require external foundational models or Einstein Studio.

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.

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.

Agent 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.

For more details, refer to Salesforce documentation on Einstein Work Summaries.