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Reg - Oct 28, 2025
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No.# The correct combination of Agentforce for Service features that addresses both needs is C. Einstein Service Replies and Work Summaries (or the Generative AI equivalents).
The solution requires two distinct actions, one for in-chat productivity and one for post-chat automation.
1. Minimizing Typing Routine Answers (In-Chat) Feature: Einstein Service Replies
Function: This Generative AI feature analyzes the ongoing conversation and automatically drafts and suggests fluent, courteous, and contextually relevant full responses for the agent to review, edit, and send with a single click. This directly minimizes the time the agent spends typing routine answers.
2. Suggesting Values for Case Fields (Post-Chat Analysis) Feature: Work Summaries
Function: This Generative AI feature (also known in earlier contexts as Case Summaries or the generative part of Case Wrap-Up) analyzes the entire conversation transcript (chat, voice, or messaging) and automatically generates and suggests values for key case fields, specifically Issue, Resolution, and Summary. This automation directly reduces the time spent on post-chat analysis and manual data entry.
Why the Other Options are Less Accurate A. Einstein Reply Recommendations and Case Classification:
Reply Recommendations suggests pre-written Quick Text snippets (Predictive AI), which is a slightly older method than the full-sentence generation of Service Replies (Generative AI).
Case Classification suggests values for fields like Priority and Type when the case is created (routing/triage), but Work Summaries is the specific tool that generates the full post-chat summary, issue, and resolution text that fills the case fields after the interaction is complete.
B. Einstein Reply Recommendations and Case Summaries:
Same issue with Reply Recommendations (not the preferred Generative AI feature).
Case Summaries is often a generic term or one component of Work Summaries, but it doesn't clearly articulate the task of suggesting...
The issue described is a classic problem with dynamic grounding in Generative AI:
Dynamic Grounding: A Field Generation prompt template works by pulling data from Salesforce records into the prompt instructions using merge fields (or related lists, flows, etc.). This data is the "grounding" context.
Variability by Record (B): Because the amount of data in fields like Description, Case Comments, or related record fields varies wildly from one record to the next, the total token count for the input prompt also varies.
Random Failures: Most Large Language Models (LLMs) have a fixed token limit (context window) for the combined input (prompt + grounding data) and output (generated response).
For records with short data, the total token count stays safely below the limit (Success).
For records with very long comments or descriptions, the grounding data is too large, the total token count exceeds the LLM's fixed limit, and the process fails with a token limit error (Random Failure).
This dependency on the record's specific data content is the source of the "random" failure pattern....
No.# C. Screenflow - been doing this in a project. You called Prompt Template" flow action in the Flow
B is incorrect. There's no Template-triggered prompt flow
Mmm - Oct 15, 2025
No.# Correct answer is A. Running tests risks modifying CRM data in a production environment.
This answer reflects the necessary caution that Salesforce imposes on Generative AI testing, particularly because the Agent's actions are live transactions that can modify data.
Risk of Modifying CRM Data (A): This statement is TRUE in the sense that the agent's actions (which are Flows, Apex, or Prompts) are transactional. If a test is run in a production environment or an environment with live data, and the agent's action includes a step like "Update Record" or "Create Record," the test execution will modify the actual CRM data. This is why the Testing Center environment is primarily used in sandboxes with test data.
Note: Although testing should ideally be done in a sandbox, the inherent nature of the Agent's actions is the ability to modify data, leading to this critical risk consideration.
Why the Other Options are Incorrect B. Running tests does not consume Einstein Requests. This is FALSE. All interactions that invoke the Generative AI Large Language Model (LLM)—including test runs in the Testing Center or Agent Builder—consume Einstein Requests (or Flex Credits), which are billable quota units.
C. Agentforce Testing Center can only be used in a production environment. This is FALSE. The Testing Center is available and intended for use in both sandbox and production environments, but it is heavily encouraged to perform the majority of testing in a sandbox to mitigate the risk mentioned...
Mmm - Oct 15, 2025
No.# Correct answer is A. Running tests risks modifying CRM data in a production environment.
This answer reflects the necessary caution that Salesforce imposes on Generative AI testing, particularly because the Agent's actions are live transactions that can modify data.
Risk of Modifying CRM Data (A): This statement is TRUE in the sense that the agent's actions (which are Flows, Apex, or Prompts) are transactional. If a test is run in a production environment or an environment with live data, and the agent's action includes a step like "Update Record" or "Create Record," the test execution will modify the actual CRM data. This is why the Testing Center environment is primarily used in sandboxes with test data.
Note: Although testing should ideally be done in a sandbox, the inherent nature of the Agent's actions is the ability to modify data, leading to this critical risk consideration.
Why the Other Options are Incorrect B. Running tests does not consume Einstein Requests. This is FALSE. All interactions that invoke the Generative AI Large Language Model (LLM)—including test runs in the Testing Center or Agent Builder—consume Einstein Requests (or Flex Credits), which are billable quota units.
C. Agentforce Testing Center can only be used in a production environment. This is FALSE. The Testing Center is available and intended for use in both sandbox and production environments, but it is heavily encouraged to perform the majority of testing in a sandbox to mitigate the risk mentioned in Option A....
Mmm - Oct 15, 2025
No.# The correct preparation required is B. Create a field set for all the fields to be grounded.
While the Record Snapshots feature is intended to simplify grounding by using data visible on the user's page, the explicit, best-practice configuration for defining which data fields are allowed to be retrieved by the generative AI is the Field Set.
Field Set (B): Creating a Field Set is the mechanism used to curate and lock down the specific collection of fields from the master record that are safe and necessary for the Large Language Model (LLM) to access. This is done to ensure data privacy and to prevent sending unnecessary fields (which consume LLM tokens) to the model.
Page Layout (A): The Record Snapshots feature does consult the Page Layout to determine which related lists (and their record limits) are included, but relying solely on the page layout for data grounding is less secure and less precise than using a Field Set.
Dynamic Forms (C): Dynamic Forms are a prerequisite for displaying the Field Generation prompt icon directly on a field, but they are not the mechanism for defining the data included in the Record Snapshots grounding resource itself....
Jared - Oct 14, 2025
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Myra - Oct 12, 2025
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No.# BELOW IS THE AGENTFORCE RESPONSE TO THIS QUESTION, SO CORRECT ANSWER IS A
To leverage the Record Snapshots grounding feature in a prompt template, the following preparation is required:
Configure the page layout of the master record type. Record Snapshots use the data available on the user's page layout for an object. The configuration of the page layout impacts which data is used in the snapshot resolution. This ensures that the data visible to the user is included in the grounding process. Additional details:
Record Snapshots allow you to include relevant data for grounding with one click, instead of selecting multiple fields and related lists individually. The account record snapshot output may include additional grounding data, such as key account fields, products, top opportunities, statistics on open cases, and past activities, independent of the page layout when available.
Recent Comments (The most recent comments are at the top.)
Passed Agentforce-Specialist exam! Have no words to thank you! I recommend you everyone I know. So useful, fast, easy and comfortable Agentforce-Specialist exam questions! You are the best!
No.# A is correct!
No.# I revisited this question and I found out that the correct answer is:
A. Einstein Reply Recommendations and Case Classification
it can be C if it's needed to be grounded in Knowledge
No.# Correct answer : C
No.# The correct combination of Agentforce for Service features that addresses both needs is
C. Einstein Service Replies and Work Summaries (or the Generative AI equivalents).
The solution requires two distinct actions, one for in-chat productivity and one for post-chat automation.
1. Minimizing Typing Routine Answers (In-Chat)
Feature: Einstein Service Replies
Function:
This Generative AI feature analyzes the ongoing conversation and automatically drafts and suggests fluent, courteous, and contextually relevant full responses for the agent to review, edit, and send with a single click. This directly minimizes the time the agent spends typing routine answers.
2. Suggesting Values for Case Fields (Post-Chat Analysis)
Feature: Work Summaries
Function:
This Generative AI feature (also known in earlier contexts as Case Summaries or the generative part of Case Wrap-Up) analyzes the entire conversation transcript (chat, voice, or messaging) and automatically generates and suggests values for key case fields, specifically Issue, Resolution, and Summary. This automation directly reduces the time spent on post-chat analysis and manual data entry.
Why the Other Options are Less Accurate
A. Einstein Reply Recommendations and Case Classification:
Reply Recommendations suggests pre-written Quick Text snippets (Predictive AI), which is a slightly older method than the full-sentence generation of Service Replies (Generative AI).
Case Classification suggests values for fields like Priority and Type when the case is created (routing/triage), but Work Summaries is the specific tool that generates the full post-chat summary, issue, and resolution text that fills the case fields after the interaction is complete.
B. Einstein Reply Recommendations and Case Summaries:
Same issue with Reply Recommendations (not the preferred Generative AI feature).
Case Summaries is often a generic term or one component of Work Summaries, but it doesn't clearly articulate the task of suggesting...
No.# Agree. B is the correct answer:
The issue described is a classic problem with dynamic grounding in Generative AI:
Dynamic Grounding: A Field Generation prompt template works by pulling data from Salesforce records into the prompt instructions using merge fields (or related lists, flows, etc.). This data is the "grounding" context.
Variability by Record (B): Because the amount of data in fields like Description, Case Comments, or related record fields varies wildly from one record to the next, the total token count for the input prompt also varies.
Random Failures: Most Large Language Models (LLMs) have a fixed token limit (context window) for the combined input (prompt + grounding data) and output (generated response).
For records with short data, the total token count stays safely below the limit (Success).
For records with very long comments or descriptions, the grounding data is too large, the total token count exceeds the LLM's fixed limit, and the process fails with a token limit error (Random Failure).
This dependency on the record's specific data content is the source of the "random" failure pattern....
No.# Agree. B is the correct answer
No.# C. Screenflow - been doing this in a project. You called Prompt Template" flow action in the Flow
B is incorrect. There's no Template-triggered prompt flow
No.# Correct answer is A. Running tests risks modifying CRM data in a production environment.
This answer reflects the necessary caution that Salesforce imposes on Generative AI testing, particularly because the Agent's actions are live transactions that can modify data.
Risk of Modifying CRM Data (A):
This statement is TRUE in the sense that the agent's actions (which are Flows, Apex, or Prompts) are transactional. If a test is run in a production environment or an environment with live data, and the agent's action includes a step like "Update Record" or "Create Record," the test execution will modify the actual CRM data. This is why the Testing Center environment is primarily used in sandboxes with test data.
Note: Although testing should ideally be done in a sandbox, the inherent nature of the Agent's actions is the ability to modify data, leading to this critical risk consideration.
Why the Other Options are Incorrect
B. Running tests does not consume Einstein Requests.
This is FALSE. All interactions that invoke the Generative AI Large Language Model (LLM)—including test runs in the Testing Center or Agent Builder—consume Einstein Requests (or Flex Credits), which are billable quota units.
C. Agentforce Testing Center can only be used in a production environment.
This is FALSE. The Testing Center is available and intended for use in both sandbox and production environments, but it is heavily encouraged to perform the majority of testing in a sandbox to mitigate the risk mentioned...
No.# Correct answer is A. Running tests risks modifying CRM data in a production environment.
This answer reflects the necessary caution that Salesforce imposes on Generative AI testing, particularly because the Agent's actions are live transactions that can modify data.
Risk of Modifying CRM Data (A):
This statement is TRUE in the sense that the agent's actions (which are Flows, Apex, or Prompts) are transactional. If a test is run in a production environment or an environment with live data, and the agent's action includes a step like "Update Record" or "Create Record," the test execution will modify the actual CRM data. This is why the Testing Center environment is primarily used in sandboxes with test data.
Note: Although testing should ideally be done in a sandbox, the inherent nature of the Agent's actions is the ability to modify data, leading to this critical risk consideration.
Why the Other Options are Incorrect
B. Running tests does not consume Einstein Requests.
This is FALSE. All interactions that invoke the Generative AI Large Language Model (LLM)—including test runs in the Testing Center or Agent Builder—consume Einstein Requests (or Flex Credits), which are billable quota units.
C. Agentforce Testing Center can only be used in a production environment.
This is FALSE. The Testing Center is available and intended for use in both sandbox and production environments, but it is heavily encouraged to perform the majority of testing in a sandbox to mitigate the risk mentioned in Option A....
No.# The correct preparation required is B. Create a field set for all the fields to be grounded.
While the Record Snapshots feature is intended to simplify grounding by using data visible on the user's page, the explicit, best-practice configuration for defining which data fields are allowed to be retrieved by the generative AI is the Field Set.
Field Set (B):
Creating a Field Set is the mechanism used to curate and lock down the specific collection of fields from the master record that are safe and necessary for the Large Language Model (LLM) to access. This is done to ensure data privacy and to prevent sending unnecessary fields (which consume LLM tokens) to the model.
Page Layout (A):
The Record Snapshots feature does consult the Page Layout to determine which related lists (and their record limits) are included, but relying solely on the page layout for data grounding is less secure and less precise than using a Field Set.
Dynamic Forms (C):
Dynamic Forms are a prerequisite for displaying the Field Generation prompt icon directly on a field, but they are not the mechanism for defining the data included in the Record Snapshots grounding resource itself....
I took the test yesterday and passed Agentforce-Specialist with a perfect score.
I am glad I found freecram on time.
It is very useful and you are bound to pass for sure. I passed mine with the guide of the Agentforce-Specialist exam questions yesterday. Wonderful purchase!
No.# Ans is A please refer https://help.salesforce.com/s/articleView?id=ai.agent_testing_center.htm&type=5
No.# B is the correct answer
No.# BELOW IS THE AGENTFORCE RESPONSE TO THIS QUESTION,
SO CORRECT ANSWER IS A
To leverage the Record Snapshots grounding feature in a prompt template, the following preparation is required:
Configure the page layout of the master record type.
Record Snapshots use the data available on the user's page layout for an object. The configuration of the page layout impacts which data is used in the snapshot resolution. This ensures that the data visible to the user is included in the grounding process.
Additional details:
Record Snapshots allow you to include relevant data for grounding with one click, instead of selecting multiple fields and related lists individually.
The account record snapshot output may include additional grounding data, such as key account fields, products, top opportunities, statistics on open cases, and past activities, independent of the page layout when available.
No.# B is correct as Field Set is a prerequisite.
No.# A is the correct answer
No.# B is the correct answer