Agentforce-Specialist Practice Test Questions

Explanation

When working with prompt templates in Salesforce (e.g. Einstein Copilot / Prompt Builder), each prompt template supports versioning.

✅ Immutability means:
Once a version of a prompt template is saved and activated, it is locked and cannot be edited.

If you need to make changes:
You must create a new version of the template.
Edit that new version before activating it.

This protects:
Audit trails (knowing exactly what instructions were used at a point in time).
Stability of production systems relying on a specific prompt version.

Hence, an immutable prompt template version:
C. Prompt template version is activated; no further changes can be saved to that version.

Why the other options are incorrect:

Option A (Only the latest version can be activated):
You can choose to activate any version you want.
Older versions can be reactivated if needed.

Option B (Every modification automatically saves a new version):
Changes do not automatically save as a new version.
You explicitly choose to create a new version.

🔗 Reference

Salesforce Help — Work with Prompt Templates and Versioning
Salesforce Developer Docs — Prompt Template Versioning
Salesforce Blog — Best Practices for Prompt Templates

Explanation

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.

Explanation:

Universal Containers (UC) is facing a relevance issue in AI-powered search results—agents are getting outdated or irrelevant documents. To solve this, UC needs fine-grained control over what content the assistant retrieves from the Data Cloud search index.

Why B is Correct:

Einstein Studio allows you to build custom retrievers, which are specialized search components that can apply filters and logic before passing context to the LLM.
By creating a custom retriever, UC can:
1. Filter by publication date (e.g., exclude documents older than a year).
2. Filter by product line or version (e.g., show results only for current products).
This results in more relevant, context-aware, and accurate AI responses for support agents.

A. Modify the search index to only store documents from the last year and remove older records
❌ Too aggressive and risky – Deleting data from the search index is not flexible, and older documents might still be useful for historical or niche cases. This also sacrifices data longevity and auditability.

C. Use the default retriever, as it already searches the entire search index and provides broad coverage
❌ Incorrect – The default retriever lacks filtering capabilities and contributes to the irrelevance problem described in the question. It’s useful for general use cases but not ideal when precise control is needed.

Explanation:

To use a Field Generation prompt template (which auto-populates fields using AI), the AgentForce Specialist must verify:

API Version 59.0 or Higher
Generative AI features (like Field Generation) require API version 59.0+ due to underlying infrastructure updates.
Older API versions lack the necessary metadata support.

Why Not the Other Options?

A. "Rich text field with 255+ characters":
Incorrect. Field Generation works with any text-type field (e.g., Text Area, Long Text Area), not just rich text fields. Length limits depend on the LLM’s token constraints, not a fixed character count.

C. "Dynamic Forms on Lightning pages":
While Dynamic Forms improve field layouts, they are not required for Field Generation. The feature works on any page where the field is visible.

Explanation:

Universal Containers wants to ensure that their AI-powered service agent:

1. Retrieves proprietary PDFs (like policy documents)
2. Grounds responses in approved company data, not generic LLM responses

The Agentforce Data Library is designed specifically for this purpose — it enables AI agents to retrieve and ground responses using curated, trusted company data (such as PDFs, knowledge articles, and documentation), while ensuring the large language model (LLM) doesn't hallucinate or use irrelevant external information.

Why A is Correct:

Agentforce Data Library allows you to upload proprietary content (PDFs, docs, etc.) and make it accessible to the LLM via retrieval-augmented generation (RAG).
It ensures that AI responses are grounded in your trusted content, not public LLM training data.
It also provides better relevance, filtering, and control over what the AI uses when answering customer questions.

B. Expand the AI agent's scope to search all Salesforce records
❌ Incorrect – While Salesforce records are important, they do not include proprietary PDFs unless those documents are explicitly stored in a searchable content format. This option does not address the PDF retrieval need.

C. Add the files to the content, and then select the data library option
❌ Partially correct, but not the first step – This would be something you do after the Data Library is set up. The first step is to set up the Agentforce Data Library, into which you then add your PDF files.

Explanation:

To customize the standard case summary response in AgentForce, the AgentForce Specialist should:

Create a Custom Record Summary Prompt Template
This allows the team to define the exact format, tone, and content of the summary (e.g., include specific fields, exclude irrelevant details, or add branding).
Custom templates override default summaries while leveraging grounding (e.g., {{Case.Description}}).

Why Not the Other Options?

B. "Summarize the Case with a standard Agent action":
Standard actions provide fixed, non-customizable outputs. They won’t meet unique business requirements.

C. "Customize the standard Record Summary template":
Standard templates cannot be edited. You must create a new custom template instead.

Steps to Implement:

. Navigate to Prompt Templates in Setup.
. Select "Record Summary" as the template type.
. Choose the Case object and define the prompt (e.g., "Summarize this case, prioritizing the last 3 comments and status changes.").
. Test and deploy to agents.

This ensures summaries align with team workflows and customer needs.

Explanation:

When building a Flex Prompt Template in Salesforce and you want to use data returned by a Flow, you need to use the "Add Flow Instructions" option.
This step allows the Flex Prompt Template to:

1. Invoke a Flow (such as an autolaunched Flow),
2. Pass parameters to the Flow (if needed),
3. Receive the output from the Flow,
4. And then use that Flow output in the prompt generation.

A. Add Flex Instructions
❌ Incorrect – This is not an actual option in the Flex Prompt Template configuration. It seems to be a distractor based on the name "Flex."

B. Add Prompt Instructions
❌ Incorrect – This step defines the actual prompt that gets sent to the LLM, including merge fields and prompt logic. However, it does not handle data retrieval or Flow interaction.

C. Add Flow Instructions
✅ Correct – This is the element used to connect a Flow to your Flex Prompt Template so that its data can be incorporated into the AI response.

Explanation

UC wants:
Automatic post-call insights.
Detection of:
. Competitor mentions.
. Product mentions.
. Custom phrases (keywords).
Visibility for sales teams after calls.

✅ The Salesforce feature designed specifically for this is Call Insights, which is part of Einstein Conversation Insights (ECI).

What is Call Insights?
Part of ECI’s AI analysis of voice and video calls.

Detects:
Products and competitors.
Custom keywords you configure (e.g. promotions, discounts, objections).
Coaching topics like pricing discussions, next steps, objections.

Provides automated tags and flags on call records.

Helps sales teams:
. Identify competitive threats.
. Understand product interest.
. Surface trends across calls.

Call Insights automatically populates these insights post-call so sales reps and managers can quickly review key topics without listening to the entire recording.
Hence, Option C is correct.

Why the other options are incorrect:

Option A (Call Summaries):
Provides a concise summary of the call content.
Useful for quick reading.
Does not automatically tag mentions of competitors, products, or keywords.

Option B (Call Explorer):

Lets users search and filter call recordings.
Useful for manual exploration.
Not the feature that automatically detects and tags specific topics in calls.

🔗 Reference

Salesforce Help — Einstein Conversation Insights Overview
Salesforce Blog — How Conversation Insights Helps Sales Teams

Explanation

The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the Agentforce Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.

Option B (agent performance score) would likely involve predictive modeling, not prompt generation.

Option C (estimating support ticket volume) would require data analysis and predictive tools, not prompt building.

For more details, refer to Salesforce’s Prompt Builder documentation for generative AI content creation.

Explanation:

To improve service center productivity using Einstein Generative AI, the most relevant features are:

✅ Service Replies
. Provides AI-generated suggested responses to customer inquiries based on case context and company knowledge (e.g., Knowledge Articles).
. Helps agents respond faster and more accurately, reducing response time and ensuring consistency.

✅ Case Summaries

. Automatically generates summaries of case interactions, including emails, chats, and internal notes.
. Useful for handoffs, escalations, and wrap-up, saving agents time from having to read through entire case histories.

These features directly enhance efficiency, accuracy, and productivity in a service center environment.

Why the other options are incorrect:

B. Service Replies and Work Summaries
❌ Incorrect – “Work Summaries” is not a standard Salesforce Generative AI feature related to Service Cloud. The correct term is Case Summaries for summarizing service interactions.

C. Reply Recommendations and Sales Summaries
❌ Incorrect – “Sales Summaries” apply to Sales Cloud, not Service operations. “Reply Recommendations” is a generic term and does not directly map to a named Einstein feature like Service Replies does.