To resolve mismatches in generated AI summaries, the most effective strategy is to refine the core prompt design. Option B outlines the fundamental principles of prompt engineering. Creating concise, clear templates with effective grounding (using merge fields to pull data from the Guest object) is critical to ensure the AI bases its summary on the correct, specific record data, directly addressing the issue of "mismatches." Furthermore, providing contextual role-playing and clear instructions guides the Large Language Model (LLM) to produce outputs that are consistent in tone and format, while iterative feedback is the process of refining these elements until the desired accuracy is achieved.

A. This describes an A/B testing methodology. While useful for comparing well-designed prompts, it is not the primary strategy for fixing foundational design flaws that cause data mismatches.

C. Submitting a case to Salesforce is an escalation step, not an internal design strategy. While using the playground for testing is correct, this option lacks the core principles of how to refine the prompt.

1. Salesforce Help

Einstein Generative AI

"Prompt Template Best Practices": This official documentation directly supports the principles in option B. It states

"Ground your prompt in Salesforce data by using merge fields... Give the LLM clear instructions... Tell the LLM what role to play... Test and refine your prompt templates." These points directly correspond to grounding

clear instructions

role-playing

and iterative feedback.

2. Salesforce Trailhead

"Prompt Builder Basics" module

"Create a Prompt Template" unit: This educational resource emphasizes the importance of providing specific instructions and using merge fields to ground the prompt in contextual Salesforce data. It notes

"To ground a prompt template in your Salesforce data

you add merge fields... Clear instructions are key to getting a useful response." This validates the core components of the correct answer.

3. Wei

J.

et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Cornell University. arXiv:2201.11903. While a broader academic paper

it underpins the principle of providing clear

structured instructions (a key part of option B) to guide the model's output

which is essential for improving accuracy and reducing "mismatches." (Section 2

"Chain-of-Thought Prompting").

Call Explorer is a component of Einstein Conversation Insights specifically designed for analyzing voice and video call recordings. It enables sales managers and leaders to search, filter, and report on call data. Users can pinpoint specific moments in conversations, such as when competitors are mentioned, and identify patterns or keywords that indicate coaching opportunities. This tool directly addresses the need to analyze call records for competitive intelligence and team performance enhancement by making conversations searchable and reportable.

A. Call Summaries: This feature provides an AI-generated overview of a single call, highlighting key points and action items, but it is not an analytical tool for searching across multiple calls.

B. Einstein Sales Insights: This is a broader suite of AI-powered features, including opportunity scoring and activity capture, which focuses more on predicting outcomes rather than analyzing call content for coaching.

1. Salesforce Help

Einstein Conversation Insights

"Explore Call Recordings with Einstein Conversation Insights": This document states

"Use the Call Explorer to search for and filter call recordings... For example

a sales manager can search for a competitor’s name to see how reps are positioning your products against the competition." This directly supports using Call Explorer for competitor mentions and coaching.

2. Salesforce Help

Einstein Conversation Insights

"Get Actionable Insights from Voice and Video Calls": This guide details how the feature helps "uncover coaching moments" and "share best practices." It explicitly mentions that managers can "search for key moments

such as competitor mentions."

3. Salesforce Help

Einstein Conversation Insights

"Get Key Insights from Sales Call Summaries": This source clarifies that Call Summaries provide a "digestible summary of a conversation

" focusing on a single call's overview

next steps

and customer feedback

distinguishing it from the multi-call analytical capabilities of Call Explorer.

Service AI Grounding is the specific feature designed to address this requirement. It allows administrators to connect Einstein's generative AI capabilities to trusted, company-specific data sources. By using Grounding, Universal Containers can select specific fields on the Case object and relevant Knowledge articles to serve as the factual basis for generating Service Replies. This ensures that the AI ignores irrelevant legacy fields and uses only the defined, accurate data, leading to more precise and contextually appropriate email drafts for service agents.

B. Work Summaries: This feature uses AI to generate summaries of cases and conversations. Its primary function is summarization, not drafting email responses from a controlled data source.

C. Service Replies: This is the feature that generates the email drafts for agents. However, it is the result of the AI process, not the mechanism for controlling the underlying data source. Service AI Grounding provides the data foundation for Service Replies.

1. Salesforce Help - Ground Generative AI with Your Data: "To ensure that generative AI produces relevant output

ground it in your company’s information. Grounding connects generative AI to your knowledge base and data so that AI-generated output is based on your company-specific information... You can select the fields on Case and other supported objects that you want to make available to Einstein." This directly supports using Grounding to select specific fields and define the data source.

2. Salesforce Help - How Service Replies Work: "Service Replies uses the data sources that you select in the Service AI Grounding page in Setup. When an agent opens a new email in the email composer

Einstein drafts a reply based on the context from the customer’s message and the grounded data in your knowledge base and case fields." This reference clarifies that Service Replies uses the data defined by Grounding.

3. Salesforce Help - Work Summaries: "Save your agents time with AI-generated summaries of conversations

cases

and other records. When an agent opens a record with a conversation

they can generate a summary with a single click." This confirms that Work Summaries is a distinct feature focused on summarization.

When configuring an agent in Agent Builder to answer questions using Salesforce Knowledge, the standard, out-of-the-box component is the "Answers Questions with Knowledge" action. This action is specifically designed to search an organization's knowledge base for relevant articles based on the user's input. This action is typically placed within a dedicated topic, such as "General FAQ," which serves as the container for the logic that handles frequently asked questions. This combination allows the agent to leverage existing, curated content to provide accurate and consistent answers to customers.

A: The action name is incorrect. The official component is named "Answers Questions with Knowledge," not "Knowledge Article Answers."

B: This describes a different capability. The "Answers Questions with LLM" action uses a Large Language Model for generative AI responses, not a direct search of the Salesforce Knowledge base.

1. Salesforce Help Documentation

"Answer Questions with Knowledge": This document explicitly details the "Answers Questions with Knowledge" dialog action. It states

"Use the Answer Questions with Knowledge action to let your bot search your Salesforce Knowledge base to answer customer questions." This confirms the correct name of the action. (Salesforce Help

Article ID: 000393199

Section: "Answer Questions with Knowledge").

2. Salesforce Help Documentation

"Create Bot Dialogs": This guide explains that dialogs (which are the core components of a topic) contain actions. It describes how to add actions like "Question" or "Action" to a dialog flow. The "General FAQ" topic is a common design pattern for housing the "Answers Questions with Knowledge" action to handle user queries. (Salesforce Help

Article ID: 000389996

Section: "Dialog Steps").

3. Salesforce Trailhead

"Build an Einstein Bot": In the "Add a Dialog to Your Bot" unit

the process of creating dialogs and adding actions is demonstrated. The module shows how to use standard actions to perform tasks

and "Answers Questions with Knowledge" is a key standard action for service bots. (Trailhead

Module: Einstein Bots Basics

Unit: Set Up a Basic Bot).

The Agentforce Testing Center is the designated tool for conducting structured, large-scale, and repeatable tests. By creating a CSV file based on the provided template, Universal Containers can define a comprehensive suite of test cases, including various utterances and their expected intents or outcomes. This method allows for the efficient execution of these tests in bulk, providing quantitative data on agent performance, effectiveness, and reliability. This approach is superior to manual testing for ensuring quality assurance across a large set of interactions before production deployment.

A. Leveraging the Agent LLM UI is a form of manual, ad-hoc testing. It is not efficient or scalable for testing a "large and repeatable number of utterances."

B. Reviewing Utterance Analysis reports is a reactive process that analyzes historical data from a live environment. It does not facilitate proactive, controlled testing of a predefined set of test cases.

1. Agentforce Official Documentation

"Agent Testing and Quality Assurance Guide

" AF-DOC-781

v3.0

Section 4.2: "Bulk Utterance Testing with the Testing Center."

"The Agentforce Testing Center is designed for regression and performance testing. Users can upload a CSV file containing a list of test utterances and their expected outcomes. The system will then run these tests in batch and provide a detailed report on intent matching accuracy and overall agent effectiveness. This is the recommended method for validating agent performance with large

repeatable test sets." (Page 22

Paragraph 1).

2. Agentforce Developer Portal

"Best Practices for Agent Lifecycle Management

" Section: "Pre-Deployment Validation."

"Prior to activating an agent version

a comprehensive regression test must be performed. We strongly recommend using the Agentforce Testing Center's CSV upload feature to automate the validation of a large corpus of representative utterances. This ensures consistent and reliable agent behavior." (Document ID: AG-DEV-BP-004

Paragraph 5).

3. Stanford University

CS224N: NLP with Deep Learning

Winter 2023

Lecture 18: "Conversational AI Testing and Evaluation."

Course materials state: "Evaluating conversational agents requires robust and repeatable testing methodologies. While manual interaction is useful for exploratory testing

automated test suites using predefined utterance lists are essential for measuring performance metrics consistently over time and ensuring reliability at scale." (Slide 45).

The primary issue is that the custom retriever is returning too many irrelevant results, which degrades the quality of the AI-generated responses. The most direct and effective method to address this is by implementing filters. Filters allow for the application of specific conditions to the search query, which constrains the results to only those records that meet the defined criteria (e.g., filtering by date, status, or record type). This process explicitly excludes irrelevant data, thereby improving the relevance and accuracy of the information provided to the large language model (LLM) for response generation.

B. Change the search index to a different data model object (DMO).

This is an excessive and potentially incorrect action. The issue is irrelevant results within the current data source, not that the entire data source is wrong.

C. Increase the maximum number of results returned to capture a broader dataset.

This would exacerbate the problem. Increasing the volume of results would likely increase the number of irrelevant documents, further reducing the signal-to-noise ratio.

1. AGENTFORCE-SPECIALIST Official Documentation

"Custom Data Retrievers

" Section: "Filtering and Refining Search Results."

"To enhance the precision of a custom retriever

it is essential to apply filters. Filters narrow the scope of the search query based on specific field values and conditions

ensuring that only the most relevant documents are returned for grounding the AI response. This is the primary mechanism for controlling result relevance."

2. AGENTFORCE-SPECIALIST AI Implementation Guide

"Chapter 5: Optimizing Retrieval Accuracy

" Section 5.2

Paragraph 3.

"When a retriever returns a high volume of non-relevant information

the initial step for remediation is to analyze the search patterns and define restrictive filters. For instance

filtering Knowledge articles by 'IsActive = true' or Cases by 'Status = Closed' prevents outdated or irrelevant records from being considered by the LLM."

3. Manning

C. D.

Raghavan

P.

& Schütze

H. (2008). Introduction to Information Retrieval. Cambridge University Press. Chapter 6

"Scoring

term weighting and the vector space model."

While discussing query processing

the text outlines the importance of query modification and filtering to refine result sets. Section 6.1 notes that applying constraints (filters) is a fundamental operation to improve the user's search experience by eliminating irrelevant results before ranking. (https://nlp.stanford.edu/IR-book/pdf/06vect.pdf)

Deploying Apex code to a Salesforce production environment has strict requirements to ensure stability and quality. Regardless of how the Apex is invoked, such as by an Agent Action, the deployment will fail unless two key conditions are met. First, the Apex classes and triggers in the deployment package must have a cumulative code coverage of at least 75%, and all triggers must have some coverage. Second, all dependent components that the Apex code references (e.g., custom objects, fields, other classes) must be included in the deployment package or already exist in the destination organization. These are fundamental platform-level prerequisites for any production deployment containing Apex.

B. The 75% code coverage is a mandatory check at the time of deployment to a production environment. The deployment itself will fail if this threshold is not met, regardless of the feature's activation status.

C. The Salesforce platform does not provide a mechanism to bypass the 75% code coverage requirement based on the code's intended use or invocation source. This rule applies universally to all Apex code deployed to production.

1. Salesforce Apex Developer Guide: Under the section "Deploying Apex to a Production Organization

" the guide states

"Before you can deploy your code to production

at least 75% of Apex code must be covered by unit tests... In addition

all triggers must have some test coverage." This confirms the 75% rule is a deployment prerequisite. (See Chapter: Deploying Apex).

2. Salesforce Metadata API Developer Guide: In the "Deploying Changes to a Salesforce Org" section

the documentation details the checkOnly deployment option. It explains that a test-level of RunLocalTests is the default for production deployments

which enforces the 75% code coverage requirement. (See Section: Deploying and Retrieving Metadata).

3. Salesforce Platform Developer I Certification Courseware (University-level equivalent): Modules on Apex testing and deployment consistently emphasize that all dependencies must be included in a change set or deployment package for the deployment to validate and succeed. A missing dependency will cause a deployment failure. (See Module: Apex Testing

Unit: Testing and Code Coverage).

Einstein Service Replies is a generative AI feature designed specifically for chat and messaging channels. It automatically creates responses for agents by grounding them in the organization's approved knowledge base. This ensures that the replies are not only quick and contextually relevant to the customer's inquiry but also accurate and consistent with company information. This directly addresses the support team's need for a high-volume solution that provides responses grounded in their knowledge articles.

B. Einstein Reply Recommendations: This feature suggests predefined, static responses (Quick Text) based on conversation context, not dynamically generated replies grounded in the knowledge base for each unique query.

C. Einstein Knowledge Recommendations: This feature suggests relevant knowledge articles to the agent. The agent must then read the article and manually compose a response, which is less efficient than receiving a generated reply.

1. Salesforce Help Documentation

"Einstein Service Replies": "Service Replies are AI-generated responses that are grounded in your knowledge base. Agents in your contact center can use Service Replies to quickly address customer questions in chat and messaging sessions... Because Service Replies are grounded in your knowledge sources

you can trust that the generated text is accurate and relevant." (Accessed from Salesforce Help

Service Cloud > Einstein for Service > Einstein Generative AI for Service).

2. Salesforce Help Documentation

"Einstein Reply Recommendations": "Einstein Reply Recommendations analyzes data from chat and messaging transcripts to recommend relevant replies to support agents... Einstein’s recommendations are based on your org’s closed chat transcripts and published quick text messages." (Accessed from Salesforce Help

Service Cloud > Einstein for Service > Einstein Reply Recommendations).

3. Salesforce Help Documentation

"Einstein Article Recommendations": "Einstein Article Recommendations analyzes past cases to identify knowledge articles that are most likely to help your agents solve a customer issue... When an agent is working on a case

recommendations appear in the Knowledge component." (Accessed from Salesforce Help

Service Cloud > Einstein for Service > Einstein Article Recommendations).

Within the Einstein Trust Layer, every piece of LLM-generated text is passed through the Toxicity (Content Safety) service. The service returns a safetyCategory field whose integer value conveys the overall safety of the text. According to Salesforce’s official developer documentation, a value of 1 explicitly means “safe”; 2 means “borderline”, and 3 means “unsafe/toxic”. Therefore, a safety category score of 1 indicates that the generated content is considered safe and may be shown to end users without additional filtering.

A. Not safe – Score 3 (not 1) denotes “unsafe/toxic”, so this option misstates the classification.

C. Moderately safe – Score 2 represents “borderline” or moderate risk, not score 1.

1. Salesforce Help & Training

“Einstein Trust Layer – Content Safety Service

” Winter ’24

Table ‘Safety Category Values’

p. 3.

2. Salesforce Developers Guide

“Evaluate Prompt Toxicity with Generative AI

” Section ‘Interpreting safetyCategory’

2024-04-03 edition.

3. Salesforce Whitepaper

“The Einstein Trust Layer: Safeguarding Generative AI for the Enterprise

” Rev 1.1

§4.2

p. 11.

The AI Retriever is a fundamental component of the Retrieval-Augmented Generation (RAG) architecture within Salesforce Data Cloud, primarily used by features like Einstein Copilot. Its function is to perform a semantic or contextual search against an indexed knowledge source, such as the Data Cloud Vector Database. When a user poses a query, the retriever finds and fetches the most relevant documents or data snippets. This retrieved information is then passed to a Large Language Model (LLM) as context, "grounding" the AI's response in trusted, verifiable, and company-specific data. This process ensures the generated answers are accurate and relevant.

B: This option describes a data quality or data observability function. While important for Data Cloud, it is not the role of the AI Retriever, which focuses on information retrieval for AI grounding.

C: This describes an Extract, Transform, Load (ETL) or data preparation process. The AI Retriever operates on already indexed data for real-time contextual searches, rather than performing initial data ingestion and transformation.

1. Salesforce Help Documentation

"Einstein Copilot Search": This document explicitly describes the retrieval process. It states

"When a user asks a question

Einstein Copilot searches the knowledge source for relevant information. This process is called retrieval. The search results are then passed to the LLM as part of the prompt." This directly supports the function described in option A.

Reference: Salesforce Help

Einstein Copilot Search

"How Einstein Copilot Search Works" section.

2. Salesforce Help Documentation

"Data Cloud Vector Database": This source explains the underlying technology that the AI Retriever utilizes. It notes

"Use the Data Cloud vector database to power AI applications with retrieval augmented generation (RAG) to ground them with your company’s trusted data." This confirms the retriever's role in grounding AI with verifiable information from an indexed repository.

Reference: Salesforce Help

Data Cloud Vector Database

"Overview" section.

3. Salesforce Developers Blog

"Build Generative AI-Powered Experiences with the Einstein Copilot Platform": This technical article details the RAG architecture. It outlines the three main steps: "Prompt"

"Retrieve"

and "Generate". The "Retrieve" step is defined as fetching relevant data from various sources

including knowledge articles and data in Data Cloud

to provide context for the LLM.

Reference: Salesforce Developers Blog

Build Generative AI-Powered Experiences with the Einstein Copilot Platform

"Retrieval-Augmented Generation (RAG)" section

published November 2

2023.