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)
Universal Containers (UC) wants to ensure the effectiveness, reliability, and trust of its agents prior to
deploying them in production. UC would like to efficiently test a large and repeatable number of
utterances. What should the Agentforce Specialist recommend?
C
Explanation:
The goal of Universal Containers (UC) is to test its Agentforce agents for effectiveness, reliability, and
trust before production deployment, with a focus on efficiently handling a large and repeatable
number of utterances. Let’s evaluate each option against this requirement and Salesforce’s official
Agentforce tools and best practices.
Option A: Leverage the Agent Large Language Model (LLM) UI and test UC's agents with different
utterances prior to activating the agent.
While Agentforce leverages advanced reasoning capabilities (powered by the Atlas Reasoning
Engine), there’s no specific "Agent Large Language Model (LLM) UI" referenced in Salesforce
documentation for testing agents. Testing utterances directly within an LLM interface might imply
manual experimentation, but this approach lacks scalability and repeatability for a large number of
utterances. It’s better suited for ad-hoc testing of individual responses rather than systematic
evaluation, making it inefficient for UC’s needs.
Option B: Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports
to review effectiveness.
Deploying an agent in a QA sandbox is a valid step in the development lifecycle, as sandboxes allow
testing in a production-like environment without affecting live data. However, "Utterance Analysis
reports" is not a standard term in Agentforce documentation. Salesforce provides tools like Agent
Analytics or User Utterances dashboards for post-deployment analysis, but these are more about
monitoring live performance than pre-deployment testing. This option doesn’t explicitly address how
to efficiently test a large and repeatable number of utterances before deployment, making it less
precise for UC’s requirement.
Option C: Create a CSV file with UC's test cases in Agentforce Testing Center using the testing
template.
The Agentforce Testing Center is a dedicated tool within Agentforce Studio designed specifically for
testing autonomous AI agents. According to Salesforce documentation, Testing Center allows users to
upload a CSV file containing test cases (e.g., utterances and expected outcomes) using a provided
template. This enables the generation and execution of hundreds of synthetic interactions in parallel,
simulating real-world scenarios. The tool evaluates how the agent interprets utterances, selects
topics, and executes actions, providing detailed results for iteration. This aligns perfectly with UC’s
need for efficiency (bulk testing via CSV), repeatability (standardized test cases), and reliability
(systematic validation), ensuring the agent is production-ready. This is the recommended approach
per official guidelines.
Why Option C is Correct:
The Agentforce Testing Center is explicitly built for pre-deployment validation of agents. It supports
bulk testing by allowing users to upload a CSV with utterances, which is then processed by the Atlas
Reasoning Engine to assess accuracy and reliability. This method ensures UC can systematically test a
large dataset, refine agent instructions or topics based on results, and build trust in the agent’s
performance—all before production deployment. This aligns with Salesforce’s emphasis on testing
non-deterministic AI systems efficiently, as noted in Agentforce setup documentation and Trailhead
modules.
Reference:
Salesforce Trailhead: Get Started with Salesforce Agentforce Specialist Certification Prep – Details the
use of Agentforce Testing Center for testing agents with synthetic interactions.
Salesforce Agentforce Documentation: Agentforce Studio > Testing Center – Explains how to upload
CSV files with test cases for parallel testing.
Salesforce Help: Agentforce Setup > Testing Autonomous AI Agents – Recommends Testing Center for
pre-deployment validation of agent effectiveness and reliability.
Universal Containers wants to implement a solution in Salesforce with a custom UX that allows users
to enter a sales order number. Subsequently, the system will invoke a custom prompt template to
create and display a summary of the sales order header and sales order details. Which solution
should an Agentforce Specialist implement to meet this requirement?
C
Explanation:
Universal Containers (UC) requires a solution with a custom UX for 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 autolaunched flow and invoke the prompt template using the standard "Prompt
Template" flow action.
An autolaunched 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
autolaunched 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
autolaunched 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: Flow Builder > Prompt Template Action – Describes how to use the "Prompt
Template" action in flows to invoke Agentforce prompts.
Trailhead: Build Flows with Prompt Templates – Highlights screen flows for user-driven AI
interactions.
Agentforce Studio Documentation: Prompt Templates – Explains how prompt templates process
input data for summaries.
What considerations should an Agentforce Specialist be aware of when using Record Snapshots
grounding in a prompt template?
A
Explanation:
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data
from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant
responses. However, there are specific limitations to consider. Let’s analyze each option based on
official documentation.
Option A: Activities such as tasks and events are excluded.
According to Salesforce Agentforce documentation, when grounding a prompt template with Record
Snapshots, the data included is limited to the record’s fields and certain related objects accessible via
Data Cloud or direct Salesforce relationships. Activities (tasks and events) are not included in the
snapshot because they are stored in a separate Activity object hierarchy and are not directly part of
the primary record’s data structure. This is a key consideration for an Agentforce Specialist, as it
means the AI won’t have visibility into task or event details unless explicitly provided through other
grounding methods (e.g., custom queries). This limitation is accurate and critical to understand.
Option B: Empty data, such as fields without values or sections without limits, is filtered out.
Record Snapshots include all accessible fields on the record, regardless of whether they contain
values. Salesforce documentation does not indicate that empty fields are automatically filtered out
when grounding a prompt template. The Atlas Reasoning Engine processes the full snapshot, and
empty fields are simply treated as having no data rather than being excluded. The phrase "sections
without limits" is unclear but likely a typo or misinterpretation; it doesn’t align with any known
Agentforce behavior. This option is incorrect.
Option C: Email addresses associated with the object are excluded.
There’s no specific exclusion of email addresses in Record Snapshots grounding. If an email field
(e.g., Contact.Email or a custom email field) is part of the record and accessible to the running user, it
is included in the snapshot. Salesforce documentation does not list email addresses as a restricted
data type in this context, making this option incorrect.
Why Option A is Correct:
The exclusion of activities (tasks and events) is a documented limitation of Record Snapshots
grounding in Agentforce. This ensures specialists design prompts with awareness that activity-related
context must be sourced differently (e.g., via Data Cloud or custom logic) if needed. Options B and C
do not reflect actual Agentforce behavior per official sources.
Reference:
Salesforce Agentforce Documentation: Prompt Templates > Grounding with Record Snapshots –
Notes that activities are not included in snapshots.
Trailhead: Ground Your Agentforce Prompts – Clarifies scope of Record Snapshots data inclusion.
Salesforce Help: Agentforce Limitations – Details exclusions like activities in grounding mechanisms.
Universal Containers (UC) currently tracks Leads with a custom object. UC is preparing to implement
the Sales Development Representative (SDR) Agent. Which consideration should UC keep in mind?
A
Explanation:
Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce
Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent
designed to assist sales teams by qualifying leads and scheduling meetings. Let’s evaluate the
options based on its functionality and limitations.
Option A: Agentforce SDR only works with the standard Lead object.
Per Salesforce documentation, the Agentforce SDR Agent is specifically designed to interact with the
standard Lead object in Salesforce. It includes preconfigured logic to qualify leads, update lead
statuses, and schedule meetings, all of which rely on standard Lead fields (e.g., Lead Status, Email,
Phone). Since UC tracks leads in a custom object, this is a critical consideration—they would need to
migrate data to the standard Lead object or create a workaround (e.g., mapping custom object data
to Leads) to leverage the SDR Agent effectively. This limitation is accurate and aligns with the SDR
Agent’s out-of-the-box capabilities.
Option B: Agentforce SDR only works on Opportunities.
The SDR Agent’s primary focus is lead qualification and initial engagement, not opportunity
management. Opportunities are handled by other roles (e.g., Account Executives) and potentially
other Agentforce agents (e.g., Sales Agent), not the SDR Agent. This option is incorrect, as it
misaligns with the SDR Agent’s purpose.
Option C: Agentforce SDR only supports custom objects associated with Accounts.
There’s no evidence in Salesforce documentation that the SDR Agent supports custom objects, even
those related to Accounts. The SDR Agent is tightly coupled with the standard Lead object and does
not natively extend to custom objects, regardless of their relationships. This option is incorrect.
Why Option A is Correct:
The Agentforce SDR Agent’s reliance on the standard Lead object is a documented constraint. UC
must consider this when planning implementation, potentially requiring data migration or process
adjustments to align their custom object with the SDR Agent’s capabilities. This ensures the agent
can perform its intended functions, such as lead qualification and meeting scheduling.
Reference:
Salesforce Agentforce Documentation: SDR Agent Setup – Specifies the SDR Agent’s dependency on
the standard Lead object.
Trailhead: Explore Agentforce Sales Agents – Describes SDR Agent functionality tied to Leads.
Salesforce Help: Agentforce Prebuilt Agents – Confirms Lead object requirement for SDR Agent.
How does the AI Retriever function within Data Cloud?
A
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.
Reference:
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.