QUESTION: 21

Universal Containers has an active standard email prompt template that does not fully deliver on the business requirements.
Which steps should an Agentforce Specialist take to use the content of the standard prompt email template in question and customize it to fully meet the business requirements?

Save as New Template and edit as needed.
Clone the existing template and modify as needed.
Save as New Version and edit as needed.

Answer(s): B

Explanation:

Universal Containers (UC) has a standard email prompt template (likely a prebuilt template provided by Salesforce) that isn't meeting their needs, and they want to customize it while retaining its original content as a starting point. Let's assess the options based on Agentforce prompt template management practices.

Option A: Save as New Template and edit as needed.

In Agentforce Studio's Prompt Builder, there's no explicit "Save as New Template" option for standard templates. This phrasing suggests creating a new template from scratch, but the question specifies using the content of the existing standard template. Without a direct "save as" feature for standards, this option is imprecise and less applicable than cloning.

Option B: Clone the existing template and modify as needed.

Salesforce documentation confirms that standard prompt templates (e.g., for email drafting or summarization) can be cloned in Prompt Builder. Cloning creates a custom copy of the standard template, preserving its original content and structure while allowing modifications. The Agentforce Specialist can then edit the cloned template--adjusting instructions, grounding, or output format-- to meet UC's specific business requirements. This is the recommended approach for customizing standard templates without altering the original, making it the correct answer.

Option C: Save as New Version and edit as needed.

Prompt Builder supports versioning for custom templates, allowing users to save new versions of an existing template to track changes. However, standard templates are typically read-only and cannot be versioned directly--versioning applies to custom templates after cloning. The question implies starting with the standard template's content, so cloning precedes versioning. This option is a secondary step, not the initial action, making it incorrect.

Why Option B is Correct:

Cloning is the documented method to repurpose a standard prompt template's content while enabling customization. After cloning, the specialist can modify the new custom template (e.g., tweak the email prompt's tone, structure, or grounding) to align with UC's requirements. This preserves the original standard template and follows Salesforce best practices.

Reference:

Salesforce Agentforce Documentation: Prompt Builder > Managing Templates Details cloning standard templates for customization.

Trailhead: Build Prompt Templates in Agentforce Explains how to clone standard templates to create editable copies.

Salesforce Help: Customize Standard Prompt Templates Recommends cloning as the first step for modifying prebuilt templates.

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QUESTION: 22

What is automatically created when a custom search index is created in Data Cloud?

A retriever that shares the name of the custom search index.
A dynamic retriever to allow runtime selection of retriever parameters without manual configuration.
A predefined Apex retriever class that can be edited by a developer to meet specific needs.

Answer(s): A

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.

Reference:

Salesforce Data Cloud Documentation: Custom Search Indexes States that a retriever is auto- created with the same name as the index.

Trailhead: Data Cloud for Agentforce Explains retriever creation in the context of search indexes.

Salesforce Help: Set Up Search Indexes in Data Cloud Confirms the retriever-index relationship.

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QUESTION: 23

An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends.
What functionality allows the Agentforce Specialist to achieve this?

Agent Event Logs dashboard.
AI Audit and Feedback Data dashboard.
User Utterances dashboard.

Answer(s): C

Explanation:

The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions. Let's assess the options based on Agentforce's analytics capabilities.

Option A: Agent Event Logs dashboard.

Agent Event Logs capture detailed technical events (e.g., API calls, errors, or system-level actions) related to agent operations.
While useful for troubleshooting or monitoring system performance, they are not designed to analyze user inputs or conversational trends. This option does not meet the requirement and is incorrect.

Option B: AI Audit and Feedback Data dashboard.

There's no specific "AI Audit and Feedback Data dashboard" in Agentforce documentation. Feedback mechanisms exist (e.g., user feedback on responses), and audit trails may track changes, but no single dashboard combines these for analyzing user queries and trends. This option appears to be a misnomer and is incorrect.

Option C: User Utterances dashboard.

The User Utterances dashboard in Agentforce Analytics is specifically designed to analyze user inputs, requests, and queries. It aggregates and visualizes what users are asking the agent, identifying patterns (e.g., common topics) and trends (e.g., rising query types). Specialists can use this to refine agent instructions or topics, making it the perfect tool for this task. This is the correct answer per Salesforce documentation.

Why Option C is Correct:

The User Utterances dashboard is tailored for conversational analysis, offering insights into user interactions that align with the specialist's goal of identifying patterns and trends. It's a documented feature of Agentforce Analytics for post-deployment optimization.

Reference:

Salesforce Agentforce Documentation: Agent Analytics > User Utterances Dashboard Describes its use for analyzing user queries.

Trailhead: Monitor and Optimize Agentforce Agents Highlights the dashboard's role in trend identification.

Salesforce Help: Agentforce Dashboards Confirms User Utterances as a key tool for interaction analysis.

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QUESTION: 24

Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information.
What is a possible explanation for the poor prompt performance?

The prompt template version is incompatible with the chosen LLM.
The data being used for grounding is incorrect or incomplete.
The Einstein Trust Layer is incorrectly configured.

Answer(s): B

Explanation:

UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause. Let's evaluate the options based on Agentforce and Einstein Generative AI mechanics.

Option A: The prompt template version is incompatible with the chosen LLM.

Prompt templates in Agentforce are designed to work with the Atlas Reasoning Engine, which abstracts the underlying large language model (LLM). Salesforce manages compatibility between prompt templates and LLMs, and there's no user-facing versioning that directly ties to LLM compatibility. This option is unlikely and not a common issue per documentation.

Option B: The data being used for grounding is incorrect or incomplete.

Grounding is the process of providing context (e.g., case record data) to the AI via prompt templates. If the grounding data--sourced from Record Snapshots, Data Cloud, or other integrations--is incorrect (e.g., wrong fields mapped) or incomplete (e.g., missing key case details), the summaries will be inaccurate. For example, if the prompt relies on Case.Subject but the field is empty or not included, the output will miss critical information. This is a frequent cause of poor performance in generative AI and aligns with Salesforce troubleshooting guidance, making it the correct answer.

Option C: The Einstein Trust Layer is incorrectly configured.

The Einstein Trust Layer enforces guardrails (e.g., toxicity filtering, data masking) to ensure safe and compliant AI outputs. Misconfiguration might block content or alter tone, but it's unlikely to cause summaries to lack appropriate information unless specific fields are masked unnecessarily. This is less probable than grounding issues and not a primary explanation here.

Why Option B is Correct:

Incorrect or incomplete grounding data is a well-documented reason for subpar AI outputs in Agentforce. It directly affects the quality of case summaries, and specialists are advised to verify grounding sources (e.g., field mappings, Data Cloud queries) when troubleshooting, as per official guidelines.

Reference:

Salesforce Agentforce Documentation: Prompt Templates > Grounding Links poor outputs to grounding issues.

Trailhead: Troubleshoot Agentforce Prompts Lists incomplete data as a common problem.

Salesforce Help: Einstein Generative AI > Debugging Prompts Recommends checking grounding data first.

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