Salesforce Agentforce Specialist Practice Test - Questions Answers, Page 2

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.

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)

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.

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)

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.

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)

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.

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 (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.

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)

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.

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)

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.

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)

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.

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)

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.

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)