What is the importance of Action Instructions when creating a custom Agent action?
A
Explanation:
In Salesforce Agentforce, custom Agent actions are designed to enable AI-driven agents to perform
specific tasks within a conversational context. Action Instructions are a critical component when
creating these actions because they define the expected user experience by outlining how the action
should behave, what it should accomplish, and how it interacts with the end user. These instructions
act as a blueprint for the action’s functionality, ensuring that it aligns with the intended outcome and
provides a consistent, intuitive experience for users interacting with the agent. For example, if the
action is to "schedule a meeting," the Action Instructions might specify the steps (e.g., gather date
and time, confirm with the user) and the tone (e.g., professional, concise), shaping the user
experience.
Option B: While Action Instructions might indirectly influence how a user invokes an action (e.g., by
making it clear what inputs are needed), they are not primarily about telling the user how to call the
action in a conversation. That’s more related to user training or interface design, not the instructions
themselves.
Option C: The large language model (LLM) relies on prompts, parameters, and grounding data to
determine which action to execute, not the Action Instructions directly. The instructions guide the
action’s design, not the LLM’s decision-making process at runtime.
Thus, Option A is correct as it emphasizes the role of Action Instructions in defining the user
experience, which is foundational to creating effective custom Agent actions in Agentforce.
Reference:
Salesforce Agentforce Documentation: "Create Custom Agent Actions" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_actions.htm&type=5)
Trailhead: "Agentforce Basics" module
(https://trailhead.salesforce.com/content/learn/modules/agentforce-basics)
Universal Containers built a Field Generation prompt template that worked for many records, but
users are reporting random failures with token limit errors. What is the cause of the random nature
of this error?
B
Explanation:
In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values
by leveraging an LLM, often with grounding data from Salesforce records or external sources. The
scenario describes a Field Generation prompt template that fails intermittently with token limit
errors, indicating that the issue is tied to exceeding the LLM’s token capacity (e.g., input + output
tokens). The random nature of these failures suggests variability in the token count across different
records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g.,
customer names, descriptions, or other fields) to generate output. Since the data varies by record—
some records might have short text fields while others have lengthy ones—the total number of
tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count
exceeds the LLM’s limit (e.g., 4,096 tokens for some models), the process fails, but this only happens
for records with higher token-generating data, explaining the randomness.
Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation
does not define "Flex" as a specific template type for handling token variability in this context (there
are Flow-based templates, but they’re unrelated to token limits). This option is a distractor and not a
verified solution.
Option C: The LLM’s token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens
for advanced models) and does not vary with user demand. Demand might affect performance or
availability, but not the token limit itself.
Option B is the correct answer because it accurately identifies the dynamic nature of the prompt
template as the root cause of variable token counts leading to random failures.
Reference:
Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
Trailhead: "Build Prompt Templates for Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/build-prompt-templates-for-agentforce)
What is a valid use case for Data Cloud retrievers?
A
Explanation:
Salesforce Data Cloud integrates with Agentforce to provide real-time, unified data access for AI-
driven applications. Data Cloud retrievers are 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.
Reference:
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)
Universal Containers (UC) wants to use Generative AI Salesforce functionality to reduce Service
Agent handling time by providing recommended replies based on the existing Knowledge articles. On
which AI capability should UC train the service agents?
A
Explanation:
Service Replies (specifically Einstein Service Replies) is the Salesforce Generative AI functionality
designed to automatically draft responses for service agents in real-time, based on contextual
information, including existing knowledge articles. This directly addresses Universal Containers' need
to reduce handling time by providing recommended replies grounded in their knowledge base
For an Agentforce Data Library that contains uploaded files, what occurs once it is created and
configured?
B
Explanation:
In Salesforce Agentforce, a Data Library is 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 are indexed to 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 with Data 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.
Reference:
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)
Universal Containers (UC) is creating a new custom prompt template to populate a field with
generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and
monitored for adoption and possible enhancements. Which prompt template type should UC use
and which consideration should UC review?
A
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.
Reference:
Salesforce Agentforce Documentation: "Prompt Template Types" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
Salesforce Einstein Trust Layer Documentation: "Monitor AI with Trust Layer"
(https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm&type=5)
Trailhead: "Build Prompt Templates for Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/build-prompt-templates-for-agentforce)
An Agentforce Specialist needs to create a prompt template to fill a custom field named Latest
Opportunities Summary on the Account object with information from the three most recently
opened opportunities. How should the Agentforce Specialist gather the necessary data for the
prompt template?
B
Explanation:
In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest
Opportunities Summary" on the Account object) requires dynamic data to be fed into the template
for AI to generate meaningful output. Here, the task is to gather data from the three most recently
opened opportunities related to an account. The most robust and flexible way to achieve this is by
using a Flow (Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the
Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element
with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the
prompt template. This approach ensures precise control over the data retrieval process and can
handle complex filtering or sorting requirements.
Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in
Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they
don’t natively support querying or aggregating related list data like the three most recent
opportunities.
Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer
(perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the
Opportunity object as a resource, prompt templates don’t directly query related objects without
additional logic (e.g., a Flow), making this incorrect.
Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the
Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close
Date) for the AI to summarize.
Thus, Option B is the correct method to gather the necessary data efficiently and accurately.
Reference:
Salesforce Agentforce Documentation: "Integrate Flows with Prompt Templates" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_flow_prompt_integration.htm&type=5)
Trailhead: "Build Flows for Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/flows-for-agentforce)
Universal Containers recently launched a pilot program to integrate conversational AI into its CRM
business operations with Agentforce Agents. How should the Agentforce Specialist monitor Agents’
usability and the assignment of actions?
C
Explanation:
Monitoring the usability and action assignments of Agentforce Agents requires insights into how
agents perform, how users interact with them, and how actions are executed within conversations.
Salesforce provides Agent Analytics (Option C) as a built-in capability specifically designed for this
purpose. Agent Analytics offers dashboards and reports that track metrics such as agent response
times, user satisfaction, action invocation frequency, and success rates. This tool allows the
Agentforce Specialist to assess usability (e.g., are agents meeting user needs?) and monitor action
assignments (e.g., which actions are triggered and how often), providing actionable data to optimize
the pilot program.
Option A: Platform Debug Logs are low-level logs for troubleshooting Apex, Flows, or system
processes. They don’t provide high-level insights into agent usability or action assignments, making
this unsuitable.
Option B: The Metadata API is used for retrieving or deploying metadata (e.g., object definitions), not
runtime log data about agent performance. While Agent log data might exist, querying it via
Metadata API is not a standard or documented approach for this use case.
Option C: Agent Analytics is the dedicated solution, offering a user-friendly way to monitor
conversational AI performance without requiring custom development.
Option C is the correct choice for effectively monitoring Agentforce Agents in a pilot program.
Reference:
Salesforce Agentforce Documentation: "Agent Analytics Overview" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_analytics.htm&type=5)
Trailhead: "Agentforce for Admins"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-admins)
Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
Retrieve proprietary policy documents that are stored as PDFs.
Ensure responses are grounded in approved company data, not generic LLM knowledge.
What should UC do first?
A
Explanation:
To implement an AI-powered customer service agent that retrieves proprietary policy documents
(stored as PDFs) and ensures responses are grounded in approved company data, UC must first
establish a foundation for the AI to access and use this data. The Agentforce Data Library (Option A) is
the correct starting point. A Data Library allows UC to upload PDFs containing policy documents,
index them into Salesforce Data Cloud’s vector database, and make them available for AI retrieval.
This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its
responses in the specific, approved content from the PDFs rather than relying on generic LLM
knowledge, directly meeting UC’s requirements.
Option B: Expanding the AI agent’s scope to search all Salesforce records is too broad and
unnecessary at this stage. The requirement focuses on PDFs with policy documents, not all Salesforce
data (e.g., cases, accounts), making this premature and irrelevant as a first step.
Option C: "Add the files to the content, and then select the data library option" is vague and not a
precise process in Agentforce. While uploading files is part of setting up a Data Library, the phrasing
suggests adding files to Salesforce Content (e.g., ContentDocument) without indexing, which doesn’t
enable AI retrieval. Setting up the Data Library (A) encompasses the full process correctly.
Option A: This is the foundational step—creating a Data Library ensures the PDFs are uploaded,
indexed, and retrievable by the agent, fulfilling both retrieval and grounding needs.
Option A is the correct first step for UC to achieve its goals.
Reference:
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: "Ground AI Responses with Data Cloud"
(https://help.salesforce.com/s/articleView?id=sf.data_cloud_agentforce.htm&type=5)
A customer service representative is looking at a custom object that stores travel information. They
recently received a weather alert and now need to cancel flights for the customers that are related to
this Itinerary. The representative needs to review the Knowledge articles about canceling and
rebooking the customer flights. Which Agentforce capability helps the representative accomplish
this?
B
Explanation:
The scenario involves a customer service representative needing to cancel flights due to a weather
alert and review existing Knowledge articles for guidance on canceling and rebooking. Agentforce
provides capabilities to streamline such tasks. The most suitable option is Option B, which allows the
agent to "execute tasks based on available actions" (e.g., canceling flights via a predefined action)
while "answering questions using information from accessible Knowledge articles." This capability
leverages Agentforce’s ability to integrate Knowledge articles into the agent’s responses, enabling
the representative to ask questions (e.g., “How do I cancel a flight?”) and receive AI-generated
answers grounded in approved Knowledge content. Simultaneously, the agent can trigger actions
(e.g., a Flow to update the custom object) to perform the cancellations, meeting all requirements
efficiently.
Option A: Invoking a Flow to call external data and create a Knowledge article is unnecessary. The
representative needs to review existing articles, not create new ones, and there’s no indication
external data is required for this task.
Option B: This is correct. It combines task execution (canceling flights) with Knowledge article
retrieval, aligning with the representative’s need to act and seek guidance from existing content.
Option C: Generating a new Knowledge article based on prompts is not relevant. The representative
needs to use existing articles, not author new ones, especially in a time-sensitive weather alert
scenario.
Option B best supports the representative’s workflow in Agentforce.
Reference:
Salesforce Agentforce Documentation: "Knowledge Replies and Actions" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm&type=5)
Trailhead: "Agentforce for Service"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)
Universal Containers wants to reduce overall customer support handling time by minimizing the time
spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by
suggesting values for case fields. Which combination of Agentforce for Service features enables this
effort?
B
Explanation:
Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in-
chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field
values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification
(Option A) are the ideal combination to achieve this.
Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on
chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies
for common questions, it significantly reduces the time spent typing routine answers, directly
addressing UC’s first goal.
Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and
suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By
automating field population, it reduces post-chat analysis time, fulfilling UC’s second goal.
Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries"
generates a summary of the case rather than suggesting specific field values. Summaries are useful
for documentation but don’t directly reduce post-chat field entry time.
Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a
distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work
orders or broader tasks, not case field suggestions in a chat context.
Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and
post-chat automation (Case Classification).
Thus, Option A is the correct answer for UC’s needs.
Reference:
Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5)
Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.case_classification.htm&type=5)
Trailhead: "Agentforce for Service"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)
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
Explanation:
In Salesforce Agentforce, a custom retriever is 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 to define 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.
Reference:
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)
When creating a custom retriever in Einstein Studio, which step is considered essential?
A
Explanation:
In Salesforce’s Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever
involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is
defining the foundation of the retriever: selecting the search index, specifying the data model object
(DMO), and identifying the data space (Option A). These elements establish where and what the
retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever
queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing
the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate—they enhance precision but aren’t
mandatory for the retriever to function. This step is foundational because without it, the retriever
lacks a target dataset, rendering it unusable.
Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping
the retriever’s output, but it’s a secondary step. The retriever must first know where to search (A)
before output can be configured.
Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking
method), which are valuable but not essential. A basic retriever can operate without specifying
search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and
data space.
Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in
Einstein Studio.
Reference:
Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
Trailhead: "Einstein Studio for Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/einstein-studio-for-agentforce)
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?
B
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs:
Resolution and Response. These terms relate to how the prompt is processed and evaluated,
particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and
auditability. The Resolution text specifically 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 template
preview.
Reference:
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)
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI-
driven training content. However, users report that the AI frequently returns outdated documents.
Which corrective action should UC implement to improve content relevancy?
B
Explanation:
UC’s issue is that their file 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.
Reference:
Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
Salesforce Data Cloud Documentation: "Filter Retrieval for AI"
(https://help.salesforce.com/s/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)
Trailhead: "Manage Data Libraries in Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-data-libraries)