QUESTION: 13

When creating a custom retriever in Einstein Studio, which step is considered essential?

Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.
Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.
Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.

Answer(s): 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)

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QUESTION: 14

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?

It shows the full text that is sent to the Trust Layer.
It shows the response from the LLM based on the sample record.
It shows which sensitive data is masked before it is sent to the LLM.

Answer(s): 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)

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QUESTION: 15

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?

Switch the data library source from file uploads to a Knowledge-based data library, because Salesforce Knowledge bases automatically manage document recency, ensuring current documents are returned.
Configure a custom retriever that includes a filter condition limiting retrieval to documents updated within a defined recent period, ensuring that only current content is used for AI responses.
Continue using the default retriever without filters, because periodic re-uploads will eventually phase out outdated documents without further configuration or the need for custom retrievers.

Answer(s): 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)

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QUESTION: 16

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?

Leverage the Agent Large Language Model (LLM) UI and test UC's agents with different utterances prior to activating the agent.
Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.
Create a CSV file with UC's test cases in Agentforce Testing Center using the testing template.

Answer(s): 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.

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