Agentforce-Specialist Exam Questions

Explanation:

Comprehensive and Detailed In-Depth Explanation: Universal Containers (UC) requires a solution with a custom UXfor users to input a sales order number, followed by invoking a custom prompt template to generate and display a summary. Let’s evaluate each option based on this requirement and Salesforce Agentforce capabilities.

Option A: Create an auto launched flow and invoke the prompt template using the standard "Prompt Template" flow action. An auto launched flow is a background process that runs without user interaction, triggered by events like record updates or platform events. While it can invoke a prompt template using the "Prompt Template" flow action (available in Flow Builder to integrate Agentforce prompts), it lacks a user interface. Since UC explicitly needs a custom UX for users to enter a sales order number, an auto launched flow cannot meet this requirement, as it doesn’t provide a way for users to input data directly.

Option B: Create a template-triggered prompt flow and invoke the prompt template using the standard "Prompt Template" flow action. There’s no such thing as a "template-triggered prompt flow" in Salesforce terminology. This appears to be a misnomer or typo in the original question. Prompt templates in Agentforce are reusable configurations that define how an AI processes input data, but they are not a type of flow. Flows (like auto launched or screen flows) can invoke prompt templates, but "template-triggered" is not a recognized flow type in Salesforce documentation. This option is invalid due to its inaccurate framing.

Option C: Create a screen flow to collect the sales order number and invoke the prompt template using the standard "Prompt Template" flow action. A screen flow provides a customizable user interface within Salesforce, allowing users to input data (e.g., a sales order number) via input fields. The "Prompt Template" flow action, available in Flow Builder, enables integration with Agentforce by passing user input (the sales order number) to a custom prompt template. The prompt template can then query related data (e.g., sales order header and details) and generate a summary, which can be displayed back to the user on a subsequent screen. This solution meets UC’s need for a custom UX and seamless integration with Agentforce prompts, making it the best fit.

Why Option C is Correct: Screen flows are ideal for scenarios requiring user interaction and custom interfaces, as outlined in Salesforce Flow documentation. The "Prompt Template" flow action enables Agentforce’s AI capabilities within the flow, allowing UC to collect the sales order number, process it via a prompt template, and display the result—all within a single, user-friendly solution. This aligns with Agentforce best practices for integrating AI-driven summaries into user workflows.

Explanation:

Comprehensive and Detailed In-Depth Explanation: UC’s development team needs to identify a core component of a Custom Agent Action in Agent Builder. Let’s assess the options.

Option A: Action Triggers "Action Triggers" isn’t a term used in Agentforce Custom Agent Action configuration. Actions are invoked by topics or plans, not standalone triggers, making this incorrect.

Option B: Instructions are a core component of a Custom Agent Action in Agentforce. Defined in Agent Builder, they guide the Atlas Reasoning Engine on how to execute the action (e.g., what to do with inputs, how to process data). Reviewing the instructions helps the team understand the action’s purpose and logic, making this the correct answer.

Option C: Output Types While outputs are part of an action’s result, "Output Types" isn’t a distinct configuration element in Agent Builder. Outputs are determined by the action’s execution (e.g., Flow or Apex), not a separate setting, making this less core and incorrect.

Why Option B is Correct: Instructions are a fundamental component of Custom Agent Actions, providing the AI’s execution directives, as per Salesforce documentation.

Explanation:

Comprehensive and Detailed In-Depth Explanation: Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC’s requirement.

Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas. The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn’t adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.

Option A: Correct. "Field Generation" matches the use case, and "Dynamic Fields" is a key consideration to ensure flexibility and auditability with the Trust Layer.

Option B: "Field Generation" is correct, but "Dynamic Forms" is unrelated. Dynamic Forms is a UI feature for customizing page layouts, not a prompt template setting, making this option incorrect.

Option C: "Flex" templates are more general-purpose and not specifically tailored for field population tasks. While Dynamic Fields could apply, Field Generation is the better fit for UC’s stated goal.

Option A is the best choice, as it pairs the appropriate template type (Field Generation) with a relevant consideration (Dynamic Fields) for UC’s scenario with the Einstein Trust Layer.

Explanation:

Comprehensive and Detailed In-Depth Explanation: UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria. Let’s evaluate.

Option A: Enable the agent to search all internal records and past customer inquiries. Searching all records and inquiries risks irrelevant or outdated responses, conflicting with the need for published Knowledge grounding and immediate updates. This lacks specificity, making it incorrect.

Option B: Set up an Agentforce Data Library to store and index policy documents for AI retrieval. The Agentforce Data Library integrates with Salesforce Knowledge, indexing HR policies, compliance guidelines, and procedures for semantic search. It ensures grounding in published Knowledge articles, and updates (e.g., new article versions) are reflected instantly without reconfiguration, as the library syncs with Knowledge automatically. This meets all UC requirements, making it the correct answer.

Option C: Manually add policy responses into the AI model to prevent hallucinations. Manually embedding responses into the model isn’t feasible—Agentforce uses pretrained LLMs, not custom training. It also doesn’t support real-time updates, making this incorrect.

Why Option B is Correct:The Data Library meets all criteria—semantic search, Knowledge grounding, and instant updates—per Salesforce’s recommended approach.

Explanation:

Comprehensive and Detailed In-Depth Explanation: UC is deploying an Agentforce Service Agent with multiple topics and actions to production. Let’s assess deployment considerations. Option A: Deploy agent components without a test run in staging, relying on production data for reliable results. Sandbox configuration alone ensures seamless production deployment. Skipping staging tests is risky and against best practices. Sandbox configuration doesn’t guarantee production success without validation, making this incorrect.

Option B: Ensure all dependencies are included, Apex classes meet 75% test coverage, and configuration settings are aligned with production. Plan for version management and post- deployment activation. This is a comprehensive approach: dependencies (e.g., flows, Apex) must be deployed, Apex requires 75% coverage, and production settings (e.g., permissions, channels) must align. Version management tracks changes, and post-deployment activation ensures controlled rollout. This aligns with Salesforce deployment best practices for Agentforce, making it the correct answer.

Option C: Deploy flows or Apex after agents, topics, and Agent Actions to avoid deployment failures and potential production agent issues requiring complete redeployment. Deploying components separately risks failures (e.g., actions needing flows failing). All components should deploy together for consistency, making this incorrect.

Why Option B is Correct: Option B covers all critical deployment considerations for a robust Agentforce rollout, as per Salesforce guidelines.

Explanation:

Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.

Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.

Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.
Thus, Agent Analytics is the correct recommendation as it offers actionable insights to drive agent adoption and productivity.

Explanation

In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.

Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agent force Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.

Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.

Explanation

Comprehensive and Detailed In-Depth Explanation: UC’s issue is that theirfile upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud’s vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter(Option B). In Agentforce, a custom retriever allows UC to define specific conditions—such as a filter on a "Last Modified Date" or similar timestamp field—to limit retrieval to documents updated within a recent period (e.g., last 6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.

Option A: Switching to a Knowledge-based Data Library(using Salesforce Knowledge articles) could work, as Knowledge articles have versioning and expiration features to manage recency. However, this assumes UC’s training content is already in Knowledge articles (not PDFs) and requires migrating all uploaded files, which is a significant shift not justified by the question’s context. File-based libraries are still viable with proper filtering.

Option B: This is the best corrective action. A custom retriever with a date filter leverages the existing file-based library, refining retrieval without changing the data source, making it practical and targeted.

Option C: Relying on periodic re-uploads with the default retriever is passive and inefficient. It doesn’t guarantee recency (old files remain indexed until manually removed)and requires ongoing manual effort, failing to proactively solve the issue.
Option B provides a precise, scalable solution to ensure content relevancy in UC’s AI-driven training system.