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

To measure adoption and usability of Agents across the organization, Agentforce Analytics is the right tool. It provides dashboards and reports that track how Agents are being used, which actions are triggered most often, and overall performance trends. This data helps organizations drive adoption by identifying gaps, monitoring usage, and demonstrating business value.

In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM’s token capacity (e.g., input + output tokens). The random nature of these failures suggests variability in the token count across different records, which is directly addressed by Option B.

Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record—some records might have short text fields while others have lengthy ones—the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM’s limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.

Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they’re unrelated to token limits). This option is a distractor and not a verified solution.

Option C: The LLM’s token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.

Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.

Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_prompt_templates.htm &type=5)

Trailhead: "Build Prompt Templates for Agentforce" (https://trailhead.salesforce.com/content/learn/modules/build-prompt-templates-for-agentforce)

UC’s sales reps need an AI action to draft personalized emails based on past successful communications, reducing manual review time. Let’s evaluate the standard Agent actions.

Option A: Agent Action: Summarize Record"Summarize Record" generates a summary of a record (e.g., Opportunity, Contact), useful for overviews but not for drafting emails or leveraging past communications. This doesn’t meet the requirement, making it incorrect.

Option B: Agent Action: Find Similar Opportunities"Find Similar Opportunities" identifies past deals to inform strategy, not to draft emails. It provides data, not text generation, making it incorrect.

Option C: Agent Action: Draft or Revise Sales EmailThe "Draft or Revise Sales Email" action in Agentforce for Sales (sometimes styled as "Draft Sales Email") uses the Atlas Reasoning Engine to generate personalized email content. It can analyze past successful communications (e.g., via Opportunity or Contact history) to tailor emails for renewals or deals, saving reps time. This directly addresses UC’s need, making it the correct answer.

Why Option C is Correct:

"Draft or Revise Sales Email" is a standard action designed for personalized email generation based on historical data, aligning with UC’s productivity goal per Salesforce documentation.

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.

Per the AgentForce Security and Permission Management Guide, the AgentForceServiceAgentUserPsg (Permission Set Group) controls access privileges for Service Agents, including which Salesforce objects, fields, and related data they can read or interact with.

When extending an agent’s access to additional custom fields or related objects, the documented best practice is to update the existing permission set group assigned to that agent type rather than creating new or profile-based permissions. This approach maintains centralized permission governance, ensures license alignment, and avoids conflicts or redundancy across multiple permission layers.

Option B is incorrect because creating a new permission set with an Einstein Agent License is unnecessary — the permission set group already includes license mappings. Option C is not recommended, as editing the Einstein Agent User Profile can cause system-wide effects and deviate from Salesforce’s principle of least privilege.

Therefore, the correct approach is Option A – Update the Object and Field access in the AgentForceServiceAgentUserPsg permission set group, ensuring secure and proper access for the Service Agent to custom case data.

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.

Salesforce Documentation on Managing Copilot Actions

Salesforce Agentforce Specialist Guide on Copilot Action Management

The AgentForce Observability and Diagnostics Guide details that AgentForce Session Tracing provides the most comprehensive visibility into agent operations. The documentation explains: “Session Tracing captures the entire execution flow for each agent session — including reasoning engine decisions, executed actions, prompts, gateway inputs and outputs, error logs, and final agent responses — to provide an end-to-end view of agent behavior.”

Agentforce Analytics (Option A) focuses on aggregated performance metrics like usage, engagement, and accuracy trends rather than deep operational data.

Utterance Analysis (Option B) evaluates specific interactions or conversation snippets but does not include reasoning engine or system-level traces.

Hence, Option C — AgentForce Session Tracing — is correct as it provides detailed, end-to-end diagnostic insight across all agent executions.

References (AgentForce Documents / Study Guide):

AgentForce Observability Guide: “Using Session Tracing for End-to-End Agent Visibility”

AgentForce Implementation Handbook: “Tracing Reasoning and Action Flows”

AgentForce Study Guide: “Monitoring and Debugging with Session Tracing”

For Universal Containers (UC) to offer personalized service experiences and reduce agent handling time using AI-generated responses grounded in the Knowledge base, the best solution is Einstein Service Replies for Email. This capability leverages AI to automatically generate responses to service-related emails based on historical data and the Knowledge base, ensuring accuracy and relevance while saving time for service agents.

Einstein Email Replies (option A) is more suited for sales use cases.

Einstein Generative Service Replies for Email (option C) could be a future offering, but as of now, Einstein Service Replies for Email is the correct choice for grounded, knowledge-based responses.

Einstein Service Replies Overview: