Salesforce Agentforce-Specialist - Salesforce Certified Agentforce Specialist (AI-201)

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.

The Agent-to-Agent (A2A) Protocol Overview describes A2A as a standardized framework for cross-vendor agent discovery and communication. The documentation specifies:

“A2A enables secure, interoperable communication between AI agents across vendors, platforms, and ecosystems, using standardized APIs and schemas for message exchange and capability discovery.”

This allows AgentForce agents to interact with external AI systems or partner agents while maintaining data governance and identity controls.

Option B is incorrect because auto-onboarding without contracts or trust verification is not supported.

Option C confuses A2A with the internal reasoning runtime used by AgentForce; A2A operates across systems, not within a single platform.

Therefore, Option A correctly defines the key benefit of the Agent-to-Agent protocol.

References (AgentForce Documents / Study Guide):

AgentForce Architecture Guide: “Understanding the Agent-to-Agent (A2A) Protocol”

AgentForce Interoperability Handbook: “Cross-Vendor Agent Communication Framework”

AgentForce Study Guide: “A2A Integration Standards and Benefits”

According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:

“Within the data library settings, under Knowledge Settings, enable ‘Use Public Knowledge Articles’ to ensure only publicly visible content is indexed. To display citations, enable ‘Show Sources’ so the agent links the specific articles or data records used to ground its response.”

Option A correctly reflects these two documented configuration steps.

Option B is incorrect because Salesforce explicitly supports source display for transparency through the “Show Sources” setting.

Option C incorrectly assumes that Data Categories control indexing visibility and source linking, which is handled by explicit Knowledge Settings, not categorization.

References (AgentForce Documents / Study Guide):

AgentForce Data Library Configuration Guide: “Knowledge Settings for Public Article Indexing”

AgentForce Transparency and Source Attribution Notes: “Show Sources Option”

AgentForce Study Guide: “Configuring Knowledge Visibility and Source Display”

When creating a custom action for Einstein Bots in Salesforce (including Agentforce), Action Instructions are critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.

Salesforce documentation emphasizes that Action Instructions directly impact the bot’s ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. The Einstein Bot Developer Guide highlights that refining instructions is essential for aligning the bot’s behavior with business requirements.

In contrast:

Action Name (A) is a static identifier and does not affect functionality.

Action Input (B) defines parameters passed to the action but does not dictate execution logic.

Thus, iterating on Action Instructions (C) ensures the action performs as expected.

The Agentforce Specialist needs a comprehensive view of user interactions, errors, and action issues for troubleshooting. Let’s evaluate the options.

Option A: Plan CanvasPlan Canvas in Agent Builder visualizes an agent’s execution plan for a single interaction, useful for design but not for aggregated troubleshooting data like errors or all interactions, making it incorrect.

Option B: Agent SettingsAgent Settings configure the agent (e.g., topics, channels), not provide interaction logs or error details. This is for setup, not analysis, making it incorrect.

Option C: Event LogsEvent Logs in Agentforce (accessible via Setup or Agent Analytics) record all user interactions, including errors, incorrectly triggered actions, and incomplete plans. They provide detailed telemetry (e.g., timestamps, action outcomes) for troubleshooting performance issues, making this the correct answer.

Why Option C is Correct:

Event Logs offer the full scope of interaction data needed for troubleshooting, as per Salesforce documentation.

To improve the productivity of the service center, the Agentforce Specialist should recommend the Service Replies and Case Summaries features.

Service Replies helps agents by automatically generating suggested responses to customer inquiries, reducing response time and improving efficiency.

Case Summaries provide a quick overview of case details, allowing agents to get up to speed faster on customer issues.

Work Summaries are not as relevant for direct customer service operations, and Sales Summaries are focused on sales processes, not service center productivity.

For more information, see Salesforce's Einstein Service Cloud documentation on the use of generative AI to assist customer service teams.

Why is "Salesforce Knowledge is not enabled" the correct answer?

If an administrator only sees the file upload option in Einstein Data Libraries and cannot configure a Salesforce Knowledge base, the most likely reason is that Salesforce Knowledge is not enabled in the organization.

Key Considerations for Einstein Data Libraries:

Salesforce Knowledge Integration is Optional

Einstein Data Libraries can pull knowledge data only if Salesforce Knowledge is enabled.

If Knowledge is not activated, the system will default to file uploads as the only available option.

How to Fix This Issue?

The administrator should enable Salesforce Knowledge in Setup → Knowledge Settings.

Once enabled, the option to configure Knowledge-based Data Libraries will become available.

Why Not the Other Options?

❌ A. The current Salesforce org lacks the necessary Einstein for Service permissions

Incorrect because even without certain permissions, the Knowledge option would still be visible but greyed out.

❌ C. The administrator is not using Lightning Experience

Incorrect because Einstein Data Libraries are accessible in both Classic and Lightning, and Lightning does not control Knowledge base visibility.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that Salesforce Knowledge must be enabled for Data Libraries to use Knowledge as a data source​.

Salesforce Certification Guide explicitly states that file uploads are the default option if Knowledge is not available.

Comprehensive and Detailed Explanation From Exact Extract:

The guide on RAG and search indexes indicates that if you need fine‑tuned retrieval behaviour (such as filtering by product line and ranking by recency), you should build a custom retriever. The documentation states: “You can add ranking factors such as recency and popularity at the time of index creation … Use pre‑filter fields and ranking factors.” Also: “When you create a search index, Data 360 automatically creates a default retriever … you can create custom retrievers in Einstein Studio to refine search criteria.” Hence to satisfy filtering by product line and recency ranking, the correct answer is B. Option A (default retriever) does not guarantee the filter/ranking customization; Option C (semantic embeddings with default metadata filters) may offer some filter capability but doesn’t explicitly provide ranking by recency and fine filter by product line. Thus B is correct.

UC needs to incorporate data from multiple unrelated objects (standard and custom) into a prompt template for a sales proposal. Let’s evaluate the options based on Agentforce capabilities.

Option A: Create a prompt template passing in a special custom object that connects the records temporarily.While a custom object could theoretically act as a junction to link unrelated records, this approach requires additional setup (e.g., creating the object, populating it with data via automation), and there’s no direct mechanism in Prompt Builder to "pass in" such an object to a prompt template without grounding or flow support. This is inefficient and not a native feature, making it incorrect.

Option B: Create a prompt template-triggered flow to access the data from standard and custom objects.There’s no such thing as a "prompt template-triggered flow" in Salesforce. Flows can invoke prompt templates (e.g., via the "Prompt Template" action), but the reverse—triggering a flow from a prompt template—is not a standard construct. While a flow could gather data from unrelated objects and pass it to a prompt, this option’s terminology is inaccurate, and it’s not the most direct solution, making it incorrect.

Option C: Create a Flex template to add resources with standard and custom objects as inputs.In Agentforce’s Prompt Builder, a Flex template (short for Flexible Prompt Template) allows users to define dynamic inputs, including data from multiple Salesforce objects (standard or custom), even if they’re unrelated. Resources can be added to the template (e.g., via merge fields or Data Cloud queries), enabling the prompt to pull data directly from specified objects without requiring a junction object or complex flows. This is ideal for generating a sales proposal using disparate data sources and aligns with Salesforce’s documentation on Flex templates, making it the correct answer.

Why Option C is Correct:

Flex templates are designed for scenarios requiring flexible data inputs, allowing UC to directly reference multiple unrelated objects in the prompt template. This simplifies the process and leverages Prompt Builder’s native capabilities, as outlined in Salesforce documentation.

The AgentForce Testing Center Guide specifies that all testing activity—including prompt tests, structured batch runs, and evaluation sessions—consumes AI credits, which can be monitored directly through the Testing Center interface. The documentation states: “Testing Center provides visibility into testing performance, outcomes, and credit usage. Administrators can view test credit consumption metrics to understand resource utilization across agents and environments.”

Agentforce Analytics (Option A) provides insights into performance, engagement, and operational metrics but does not directly track credit consumption tied to testing. Digital Wallet (Option B) manages AI credit balances across orgs but does not offer test-specific tracking. Therefore, Testing Center is the correct answer since it explicitly supports monitoring credit usage during agent testing activities.

References (AgentForce Documents / Study Guide):

AgentForce Testing Center Guide: “Monitoring Credit Consumption During Testing”

AgentForce Admin Handbook: “Tracking Usage and Test Costs”

AgentForce Study Guide: “Testing Resource Visibility and Credit Management”