Agentforce-Specialist Practice Test Questions

Explanation:

Record Snapshots grounding in Salesforce's Einstein for Agentforce (or more broadly, for generative AI features in Salesforce) allows AI prompts to include real-time data from Salesforce records. To control which fields are included when grounding a prompt with a record snapshot, you use a field set.
Here's a breakdown of the options:

A. Configure page layout of the master record type
❌ Incorrect – Page layouts control UI, not AI data grounding. They don’t influence what data is used in prompt templates.

B. Create a field set for all the fields to be grounded
✅ Correct – Salesforce uses field sets to define which fields from the record are included in the prompt context. This is a key step when configuring Record Snapshots grounding for prompt templates.

C. Enable and configure dynamic form for the object
❌ Incorrect – Dynamic forms also relate to UI behavior and layout on Lightning pages. They don't control what data is used for AI grounding.

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.

Reference:
Salesforce Help - Flows with Prompt Templates

Explanation:

When configuring a Custom Agent Action, one of the core components the development team must review is:

Instructions
Define how users invoke the action in natural language (e.g., "Check order status" or "Escalate this case").
Guide the LLM in mapping user requests to the correct action.

Example:

"Use this action to check shipping status. Try phrases like:
- 'Where is my order?'
- 'Has this been shipped yet?'"

Why Not the Other Options?

A. "Action Triggers":
Triggers determine when an action runs (e.g., record-triggered), but aren’t a core configurable component of the action itself.

C. "Output Types":
While important, outputs (e.g., text, record updates) are secondary to the instructions that define the action’s purpose.

Reference:
Salesforce Help - Custom Agent Actions

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.

Steps to Implement:

1. Upload policy documents to the Data Library (e.g., PDFs, FAQs).
2. Configure semantic search (Einstein Search) for natural language queries.
3. Ground the Agent’s prompts in the Data Library (e.g., {{DataLibrary.HR_Policies}}).

This ensures trusted, self-updating responses aligned with company standards.

Explanation

Deploying an Agentforce Service Agent into production—especially with multiple topics and custom Agent Actions—is a complex deployment. Here’s why Option B is correct:

✅ Key considerations when deploying Agentforce Service Agents:

Dependencies and References:
Agents, topics, and Agent Actions often reference:
. Flows
. Apex classes
. Prompt templates
. Search indexes
. Custom metadata
All referenced components must be deployed together or the agent will fail to execute in production.

Apex Test Coverage:
Salesforce requires a minimum of 75% test coverage for all Apex classes and triggers in production.
This is especially critical if your custom Agent Actions call Apex.

Environment Parity:
Ensure that settings and connected services (e.g. Data Cloud, Copilot indexes) in the sandbox/staging are mirrored in production.

Version Management:
You may need to manage different versions of:
. Prompt templates
. Agent configurations
. Flows
Deploying new versions without proper version management could break existing functionality.

Post-deployment Activation:
After deployment, Agentforce Agents are inactive by default in production.
You must manually activate the agent and test in production.

Hence, Option B is the correct and complete answer for real-world deployments.

Why the other options are incorrect:

Option A (Deploy without a test run in staging):
This is risky and not a best practice.
Testing in a sandbox or staging environment is critical to avoid production issues.

Option C (Deploy flows or Apex after agents):
That’s backwards.
Agent Actions depend on:
. Flows
. Apex classes
Those dependencies must be deployed first before the Agent can function.
Deploying agents before their supporting components would result in errors.

Therefore, Universal Containers should:
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.

🔗 Reference
Salesforce Help — Deploy Copilot Components to Production
Salesforce Developer Docs — Agentforce Deployment Best Practices
Salesforce Blog — Avoid Deployment Pitfalls for Einstein Copilot

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

To include related Opportunities data in a prompt template, the AI Specialist should:

Use Merge Fields for Default Related Lists

Prompt templates support default related list merge fields (e.g., {{Account.Opportunities}}).
This dynamically pulls all opportunities tied to the Account without custom development.

Example:
"Summarize all opportunities for {{Account.Name}}: {{Account.Opportunities}}"

Why Not the Other Options?

A. Custom related list merge fields:
Unnecessary complexity. Default merge fields work for standard related lists. Custom retrievers are only needed for external/non-standard data.

C. Formula fields:
Formula fields cannot process related lists as input. They’re for single-record calculations.

Implementation Steps:

In Prompt Builder, create a new template.
Use the merge field {{Account.Opportunities}} to ground the prompt.
Add filters if needed (e.g., {{Account.Opportunities|filter:"StageName='Closed Won'"}}).

This ensures real-time, structured opportunity data in AI-generated summaries.

Explanation:

When the data you want to ground into a prompt is stored in an external system (like an ERP), and you want to call that external service in real-time to get data, the correct grounding technique in Salesforce Agentforce is: External Services Merge Fields

A. Eternal Object Record Merge Fields
❌ Incorrect – There's a typo here (likely meant to be "External Object Record Merge Fields"). Even so, External Objects are used for Salesforce Connect, which virtually maps external data but does not call the external service in real-time during prompt execution. It also doesn't support dynamic fetch during prompt generation.

B. External Services Merge Fields
✅ Correct – This feature allows prompt templates to invoke an API call to an external system (like ERP) and use that data directly in the prompt context. It's real-time, secure, and the proper way to get dynamic external data into LLM prompts.

C. Apex Merge Fields
❌ Incorrect – Apex Merge Fields are useful for custom logic and custom data manipulation within Salesforce, but they don’t inherently connect to external systems unless you write custom callouts in Apex (which would then be abstracted behind an Apex class, not recommended for direct grounding unless needed).

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.