Salesforce
Agentforce-Specialist
Salesforce Certified Agentforce Specialist
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Exact Questions Salesforce - Agentforce-Specialist
Question #:1
Universal Containers (UC) wants to enable its sales team to use AI to suggest recommended products from its
catalog. Which type of prompt template should UC use?
A. Record summary prompt template
B. Email generation prompt template
C. Flex prompt template
Answer: C
Explanation
Comprehensive and Detailed In-Depth Explanation:UC needs an AI solution to suggest products from a
catalog for its sales team. Let’s assess the prompt template types in Prompt Builder.
Option A: Record summary prompt templateRecord summary templates generate concise
summaries of records (e.g., Case, Opportunity). They’re not designed for product recommendations,
which require dynamic logic beyond summarization, making this incorrect.
Option B: Email generation prompt templateEmail generation templates craft emails (e.g., customer
outreach). While they could mention products, they’re not optimized for standalone recommendations,
making this incorrect.
Option C: Flex prompt templateFlex prompt templates are versatile, allowing custom inputs (e.g.,
catalog data from objects or Data Cloud) and instructions (e.g., “Suggest products based on customer
preferences”). This flexibility suits UC’s need to recommend products dynamically, making it the
correct answer.
Why Option C is Correct:Flex templates offer the customization needed to suggest products from a catalog,
aligning with Salesforce’s guidance for tailored AI outputs.
References:
Salesforce Agentforce Documentation: Prompt Builder > Flex Templates– Details dynamic use cases.
Trailhead: Build Prompt Templates in Agentforce– Covers Flex for custom scenarios.
Salesforce Help: Prompt Template Types– Confirms Flex versatility.
Question #:2
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom
prompt to summarize case records. Users have reported that the case summaries generated are not returning
the appropriate information. What is a possible explanation for the poor prompt performance?
A. The prompt template version is incompatible with the chosen LLM.
Only exact questions will Pass You in Exam 1 of 10
Exact Questions Salesforce - Agentforce-Specialist
B. The data being used for grounding is incorrect or incomplete.
C. The Einstein Trust Layer is incorrectly configured.
Answer: B
Explanation
Comprehensive and Detailed In-Depth Explanation:UC’s custom prompt for summarizing case records is
underperforming, and we need to identify a likely cause. Let’s evaluate the options based on Agentforce and
Einstein Generative AI mechanics.
Option A: The prompt template version is incompatible with the chosen LLM.Prompt templates in
Agentforce are designed to work with the Atlas Reasoning Engine, which abstracts the underlying large
language model (LLM). Salesforce manages compatibility between prompt templates and LLMs, and
there’s no user-facing versioning that directly ties to LLM compatibility. This option is unlikely and not
a common issue per documentation.
Option B: The data being used for grounding is incorrect or incomplete.Grounding is the process
of providing context (e.g., case record data) to the AI via prompt templates. If the grounding data—
sourced from Record Snapshots, Data Cloud, or other integrations—is incorrect (e.g., wrong fields
mapped) or incomplete (e.g., missing key case details), the summaries will be inaccurate. For example,
if the prompt relies on Case.Subject but the field is empty or not included, the output will miss critical
information. This is a frequent cause of poor performance in generative AI and aligns with Salesforce
troubleshooting guidance, making it the correct answer.
Option C: The Einstein Trust Layer is incorrectly configured.The Einstein Trust Layer enforces
guardrails (e.g., toxicity filtering, data masking) to ensure safe and compliant AI outputs.
Misconfiguration might block content or alter tone, but it’s unlikely to cause summaries to lack
appropriate informationunless specific fields are masked unnecessarily. This is less probable than
grounding issues and not a primary explanation here.
Why Option B is Correct:Incorrect or incomplete grounding data is a well-documented reason for subpar AI
outputs in Agentforce. It directly affects the quality of case summaries, and specialists are advised to verify
grounding sources (e.g., field mappings, Data Cloud queries) when troubleshooting, as per official guidelines.
References:
Salesforce Agentforce Documentation: Prompt Templates > Grounding– Links poor outputs to
grounding issues.
Trailhead: Troubleshoot Agentforce Prompts– Lists incomplete data as a common problem.
Salesforce Help: Einstein Generative AI > Debugging Prompts– Recommends checking grounding data
first.
Question #:3
Only exact questions will Pass You in Exam 2 of 10
Exact Questions Salesforce - Agentforce-Specialist
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses.
UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What
should UC do to ensure only relevant data is retrieved?
A. Define filters to narrow the search results based on specific conditions.
B. Change the search index to a different data model object (DMO).
C. Increase the maximum number of results returned to capture a broader dataset.
Answer: A
Explanation
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, acustom retrieveris used to
fetch relevant data (e.g., from Data Cloud’s vector database or Salesforce records) to ground AI responses.
UC’s issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best
solution is todefine filters(Option A) to refine the retriever’s search criteria. Filters allow UC to specify
conditions (e.g., "only retrieve documents from the ‘Policy’ category” or “records created after a certain
date”) that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the
precision of AI-generated responses by excluding extraneous data, addressing UC’s problem effectively.
Option B: Changing the search index to a different data model object (DMO) might be relevant if the
retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the
question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is
more appropriate than switching DMOs.
Option C: Increasing the maximum number of results would worsen the issue by returning even more
data, including more irrelevant entries, contrary to UC’s goal of improving relevance.
Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved
data, making this the correct action.
Option A is the most effective step to ensure relevance in retrieved data.
References:
Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)
Question #:4
Universal Containers recently added a custom flow for processing returns and created a new Agent Action.
Which action should the company take to ensure the Agentforce Service Agent can run this new flow as part
of the new Agent Action?
A.
Only exact questions will Pass You in Exam 3 of 10
Exact Questions Salesforce - Agentforce-Specialist
A. Recreate the flow using the Agentforce agent user.
B. Assign the Manage Users permission to the Agentforce Agent user.
C. Assign the Run Flows permission to the Agentforce Agent user.
Answer: C
Explanation
Comprehensive and Detailed In-Depth Explanation:UC has created a custom flow for processing returns
and linked it to a new Agent Action for the Agentforce Service Agent, an AI-driven agent for customer
service tasks. The agent must have the ability to execute this flow. Let’s assess the options.
Option A: Recreate the flow using the Agentforce agent user.Flows are authored by admins or
developers, not "recreated" by specific users like the Agentforce agent user (a system user for agent
operations). The issue isn’t the flow’s creation context but its execution permissions. This option is
impractical and incorrect.
Option B: Assign the Manage Users permission to the Agentforce Agent user.The "Manage Users"
permission allows user management (e.g., creating or editing users), which is unrelated to running
flows. This permission is excessive and irrelevant for the Service Agent’s needs, making it incorrect.
Option C: Assign the Run Flows permission to the Agentforce Agent user.The Agentforce Service
Agent operates under a dedicated system user (e.g., "Agentforce Agent User") with a specific profile or
permission set. To execute a flow as part of an Agent Action, this user must have the "Run Flows"
permission, either via its profile or a permission set (e.g., Agentforce Service Permissions). This
ensures the agent can invoke the custom flow for processing returns, aligning with Salesforce’s security
model and Agentforce setup requirements. This is the correct answer.
Why Option C is Correct:Granting the "Run Flows" permission to the Agentforce Agent user is the
standard, documented step to enable flow execution in Agent Actions, ensuring the Service Agent can process
returns as intended.
References:
Salesforce Agentforce Documentation: Agent Builder > Custom Actions– Requires "Run Flows" for
flow-based actions.
Trailhead: Set Up Agentforce Service Agents– Lists "Run Flows" in agent user permissions.
Salesforce Help: Agentforce Security > Permissions– Confirms flow execution needs.
Question #:5
Universal Containers (UC) wants to build an Agentforce Service Agent that provides the latest, active, and
relevant policy and compliance information to customers. The agent must:
Semantically search HR policies, compliance guidelines, and company procedures.
Only exact questions will Pass You in Exam 4 of 10
Exact Questions Salesforce - Agentforce-Specialist
Ensure responses are grounded on published Knowledge.
Allow Knowledge updates to be reflected immediately without manual reconfiguration.What should
UC do to ensure the agent retrieves the right information?
A. Enable the agent to search all internal records and past customer inquiries.
B. Set up an Agentforce Data Library to store and index policy documents for AI retrieval.
C. Manually add policy responses into the AI model to prevent hallucinations.
Answer: B
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.
References:
Salesforce Agentforce Documentation: Data Library > Knowledge Integration– Details indexing and
updates.
Trailhead: Build Agents with Agentforce– Covers Data Library for accurate responses.
Salesforce Help: Grounding with Knowledge– Confirms real-time sync.
Question #:6
What is a valid use case for Data Cloud retrievers?
A. Returning relevant data from the vector database to augment a prompt.
Only exact questions will Pass You in Exam 5 of 10
Exact Questions Salesforce - Agentforce-Specialist
B. Grounding data from external websites to augment a prompt with RAG.
C. Modifying and updating data within the source systems connected to Data Cloud.
Answer: A
Explanation
Comprehensive and Detailed In-Depth Explanation:Salesforce Data Cloud integrates with Agentforce to
provide real-time, unified data access for AI-driven applications.Data Cloud retrieversare specialized
components that fetch relevant data from Data Cloud’s vector database—a storage system optimized for
semantic search and retrieval—to enhance agent responses or actions. A valid use case, as described in Option
A, is using these retrievers to return pertinent data (e.g., customer purchase history, support tickets) from the
vector database to augment a prompt. This process, often part of Retrieval-Augmented Generation (RAG),
allows the LLM to generate more accurate, context-aware responses by grounding its output in structured,
searchable data stored in Data Cloud.
Option B: Grounding data from external websites is not a primary function of Data Cloud retrievers.
While RAG can incorporate external data, Data Cloud retrievers specifically work with data within
Salesforce’s ecosystem (e.g., the vector database or harmonized data lakes), not arbitrary external
websites. This makes B incorrect.
Option C: Data Cloud retrievers are read-only mechanisms designed for data retrieval, not for
modifying or updating source systems. Updates to source systems are handled by other Salesforce tools
(e.g., Flows or Apex), not retrievers.
Option A is correct because it aligns with the core purpose of Data Cloud retrievers: enhancing prompts with
relevant, vectorized data from within Salesforce Data Cloud.
References:
Salesforce Data Cloud Documentation: "Data Cloud for Agentforce" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.data_cloud_agentforce.htm&type=5)
Trailhead: "Data Cloud Basics" module (https://trailhead.salesforce.com/content/learn/modules/data-
cloud-basics)
Question #:7
How does the AI Retriever function within Data Cloud?
A. It performs contextual searches over an indexed repository to quickly fetch the most relevant
documents, enabling grounding AI responses with trustworthy, verifiable information.
B. It monitors and aggregates data quality metrics across various data pipelines to ensure only high-
integrity data is used for strategic decision-making.
C. It automatically extracts and reformats raw data from diverse sources into standardized datasets for use
in historical trend analysis and forecasting.
Only exact questions will Pass You in Exam 6 of 10
Exact Questions Salesforce - Agentforce-Specialist
Answer: A
Explanation
Comprehensive and Detailed In-Depth Explanation:The AI Retriever is a key component in Salesforce
Data Cloud, designed to support AI-driven processes like Agentforce by retrieving relevant data. Let’s
evaluate each option based on its documented functionality.
Option A: It performs contextual searches over an indexed repository to quickly fetch the most
relevant documents, enabling grounding AI responses with trustworthy, verifiable information.
The AI Retriever in Data Cloud uses vector-based search technology to query an indexed repository (e.
g., documents, records, or ingested data) and retrieve the most relevant results based on context. It
employs embeddings to match user queries or prompts with stored data, ensuring AI responses (e.g., in
Agentforce prompt templates) are grounded in accurate, verifiable information from Data Cloud. This
enhances trustworthiness by linking outputs to source data, making it the primary function of the AI
Retriever. This aligns with Salesforce documentation and is the correct answer.
Option B: It monitors and aggregates data quality metrics across various data pipelines to ensure
only high-integrity data is used for strategic decision-making.Data quality monitoring is handled by
other Data Cloud features, such as Data Quality Analysis or ingestion validation tools, not the AI
Retriever. The Retriever’s role is retrieval, not quality assessment or pipeline management. This option
is incorrect as it misattributes functionality unrelated to the AI Retriever.
Option C: It automatically extracts and reformats raw data from diverse sources into
standardized datasets for use in historical trend analysis and forecasting.Data extraction and
standardization are part of Data Cloud’s ingestion and harmonization processes (e.g., via Data Streams
or Data Lake), not the AI Retriever’s function. The Retriever works with already-indexed data to fetch
results, not to process or reformat raw data. This option is incorrect.
Why Option A is Correct:The AI Retriever’s core purpose is to perform contextual searches over indexed
data, enabling AI grounding with reliable information. This is critical for Agentforce agents to provide
accurate responses, as outlined in Data Cloud and Agentforce documentation.
References:
Salesforce Data Cloud Documentation: AI Retriever– Describes its role in contextual searches for
grounding.
Trailhead: Data Cloud for Agentforce– Explains how the AI Retriever fetches relevant data for AI
responses.
Salesforce Help: Grounding with Data Cloud– Confirms the Retriever’s search functionality over
indexed repositories.
Question #:8
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template
they've written. They see two distinct text outputs: Resolution and Response. Which information does the
Resolution text provide?
Only exact questions will Pass You in Exam 7 of 10
Exact Questions Salesforce - Agentforce-Specialist
A. It shows the full text that is sent to the Trust Layer.
B. It shows the response from the LLM based on the sample record.
C. It shows which sensitive data is masked before it is sent to the LLM.
Answer: A
Explanation
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, when previewing a prompt
template, the interface displays two outputs:ResolutionandResponse. These terms relate to how the prompt is
processed and evaluated, particularly in the context of theEinstein Trust Layer, which ensures AI safety,
compliance, and auditability. TheResolution textspecifically refers to the full text that is sent to the Trust
Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with
grounding data, instructions, and variables) as it’s submitted to the large language model (LLM), along with
any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It’s a
comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance
purposes.
Option B: The "Response" output in the preview shows the LLM’s generated text based on the sample
record, not the Resolution. Resolution encompasses more than just the LLM response—it includes the
entire payload sent to the Trust Layer.
Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the
Resolution text doesn’t specifically isolate "which sensitive data is masked." Instead, it shows the full
text, including any masked portions, as processed by the Trust Layer—not a separate masking log.
Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer,
aligning with its role in monitoring and auditing the AI interaction.
Thus, Option A accurately describes the purpose of the Resolution text in the prompt templatepreview.
References:
Salesforce Agentforce Documentation: "Preview Prompt Templates" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.agentforce_prompt_preview.htm&type=5)
Salesforce Einstein Trust Layer Documentation: "Trust Layer Outputs" (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm&type=5)
Question #:9
For an Agentforce Data Library that contains uploaded files, what occurs once it is created and configured?
A. Indexes the uploaded files in a location specified by the user
B. Indexes the uploaded files into Data Cloud
C.
Only exact questions will Pass You in Exam 8 of 10
Exact Questions Salesforce - Agentforce-Specialist
C. Indexes the uploaded files in Salesforce File Storage
Answer: B
Explanation
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, aData Libraryis a feature that
allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven
agents. Once the Data Library is created and configured, the uploaded files areindexedto make their content
searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key
question is where this indexing occurs. Salesforce Agentforce integrates tightly withData Cloud, a unified
data platform that includes a vector database optimized for storing and indexing unstructured data like
uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud’s vector
database, enabling the AI to efficiently retrieve relevant information from them during conversations or
actions.
Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data
Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.
Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its vector
database to support AI capabilities like semantic search and content retrieval.
Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general
file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for
indexing, not basic file storage.
Thus, Option B accurately reflects the process after a Data Library is created and configured in Agentforce.
References:
Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help:https://help.salesforce.
com/s/articleView?id=sf.agentforce_data_library.htm&type=5)
Salesforce Data Cloud Documentation: "Vector Database for AI" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_vector_database.htm&type=5)
Question #:10
An Agentforce Specialist wants to troubleshoot their Agent’s performance. Where should the Agentforce
Specialist go to access all user interactions with the Agent, including Agent errors, incorrectly triggered
actions, and incomplete plans?
A. Plan Canvas
B. Agent Settings
C. Event Logs
Answer: C
Only exact questions will Pass You in Exam 9 of 10
Exact Questions Salesforce - Agentforce-Specialist
Explanation
Comprehensive and Detailed In-Depth Explanation:The Agentforce Specialist needs a comprehensive
view of user interactions, errors, and action issues for troubleshooting. Let’s evaluate the options.
Option A: Plan CanvasPlan Canvas in Agent Builder visualizes an agent’s execution plan for a single
interaction, useful for design but not for aggregated troubleshooting data like errors or all interactions,
making it incorrect.
Option B: Agent SettingsAgent Settings configure the agent (e.g., topics, channels), not provide
interaction logs or error details. This is for setup, not analysis, making it incorrect.
Option C: Event LogsEvent Logs in Agentforce (accessible via Setup or Agent Analytics) record all
user interactions, including errors, incorrectly triggered actions, and incomplete plans. They provide
detailed telemetry (e.g., timestamps, action outcomes) for troubleshooting performance issues, making
this the correct answer.
Why Option C is Correct:Event Logs offer the full scope of interaction data needed for troubleshooting, as
per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Agent Analytics > Event Logs– Details interaction and error
logging.
Trailhead: Monitor and Optimize Agentforce Agents– Recommends Event Logs for troubleshooting.
Salesforce Help: Agentforce Performance– Confirms logs for diagnostics.
Only exact questions will Pass You in Exam 10 of 10
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