Salesforce Certified Agentforce Specialist (AI-201) Salesforce Certified Agentforce Specialist Exam Practice Test

Answer : B

Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.

* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.

* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.

* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.

Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_llm.htm

Answer : B

Why is 'Automates Ingestion, Indexing, and Default Retriever Creation' the correct answer?

An Agentforce Data Library is a key component in ensuring that an AI agent provides precise and up-to-date responses by:

Automating data ingestion Brings in data from various sources.

Indexing the data Organizes it efficiently for AI retrieval.

Creating a default retriever Enables the AI to fetch relevant data dynamically when answering customer queries.

Key Features of an Agentforce Data Library:

1. Automates Data Ingestion

o Integrates real-time and historical data into Salesforce Data Cloud.

o Ensures that relevant updates are continuously fed into the AI system.

2. Indexes Data for Efficient Retrieval

o Enhances searchability for quick, context-aware responses.

o Enables fast AI response times while maintaining accuracy.

3. Creates a Default Retriever

o AI agents use the retriever to fetch the most relevant and current information.

o The retriever grounds AI-generated responses using structured and indexed data.

Why Not the Other Options?

A. Automates ingestion, taxonomical classification, and precision keyword search retrieval

* Incorrect because Agentforce does not rely on keyword searches but on indexing and AI-driven retrieval.

C. Automates ingestion and OCR processing of PDFs

* Incorrect because OCR (Optical Character Recognition) is not the primary function of an Agentforce Data Library.

* AI grounding is based on indexed and structured data, not raw OCR-extracted text.

Agentforce Specialist Reference

* Salesforce AI Specialist Material explains that Agentforce Data Libraries automate data ingestion, indexing, and retriever setup for AI-powered responses.

* Salesforce Instructions for Certification confirm that AI responses are grounded in structured and indexed Data Libraries.

Answer : C

Comprehensive and Detailed In-Depth Explanation:

UC's agent answers questions using Knowledge articles, configured in Agent Builder. Let's identify the topic and action.

* Option A: General Q&A topic and Knowledge Article Answers action.

'General Q&A' is not a standard topic name in Agentforce, and 'Knowledge Article Answers' isn't a predefined action. This lacks specificity and doesn't match documentation, making it incorrect.

* Option B: General CRM topic and Answers Questions with LLM Action.

'General CRM' isn't a default topic, and 'Answers Questions with LLM' suggests raw LLM responses, not Knowledge-grounded ones. This doesn't align with the Knowledge focus, making it incorrect.

* Option C: General FAQ topic and Answers Questions with Knowledge Action.

In Agent Builder, the 'General FAQ' topic is a common default or starting point for question-answering agents. The 'Answers Questions with Knowledge' action (sometimes styled as 'Answer with Knowledge') is a prebuilt action that retrieves and grounds responses with Knowledge articles. This matches UC's implementation and is explicitly supported in documentation, making it the correct answer.

Why Option C is Correct:

'General FAQ' and 'Answers Questions with Knowledge' are the standard topic-action pair for Knowledge-based question answering in Agentforce, per Salesforce resources.

* Salesforce Agentforce Documentation: Agent Builder > Actions -- Lists 'Answers Questions with Knowledge.'

* Trailhead: Build Agents with Agentforce -- Describes FAQ topics with Knowledge actions.

* Salesforce Help: Knowledge in Agentforce -- Confirms this configuration.

Answer : C

Comprehensive and Detailed In-Depth 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.

* 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)

Answer : C

In the Einstein Trust Layer, the Salesforce Agentforce Specialist can configure privacy data entities to be masked (Option C). This ensures sensitive or personally identifiable information (PII) is obfuscated when processed by AI models.

1. Data Masking Configuration:

o The Agentforce Specialist defines which fields or data types (e.g., email, phone number, Social Security Number) should be masked. For example, masking the Email field in a prompt response to protect user privacy.

o This is done through declarative settings in Salesforce, where entities (standard or custom fields) are flagged for masking.

2. Why Other Options Are Incorrect:

o A. Profiles exempt from masking: Exemptions are typically managed via permissions (e.g., field-level security), not directly within Einstein Trust Layer's Data Masking settings.

o B. Encryption keys for masking: Encryption is separate from masking. Masking involves obfuscation (e.g., replacing 'john@example.com' with '@'), not encryption, which uses keys to secure data.

* Einstein Trust Layer Documentation: States that Data Masking allows admins to 'define which fields should be masked to protect sensitive data.'

* Trailhead Module: 'Einstein Trust Layer Basics' explains configuring privacy entities for masking.

* Salesforce Help Article: 'Secure AI with Einstein Trust Layer' details masking configurations for privacy compliance.

Answer : A

To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice examples, the Agentforce Specialist should use a prompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.

* Option B (flex template) does not provide the ability to fetch dynamic data from Salesforce records automatically.

* Option C (manual insertion) would not allow for the dynamic and automated grounding of data required for custom prompts.

Refer to Salesforce documentation on flows and grounding for more details on integrating data into custom prompt templates.

Answer : B

To effectively manage and use prompt templates, two distinct permission sets are required:

* Prompt Template Manager: This permission set allows users to create prompt templates. It provides the necessary access to define templates, which can be shared and utilized across the organization.

* Prompt Template User: This permission set is designed for users who need to execute the templates. It provides the ability to interact with pre-designed prompts and generate outcomes based on these templates.

The Data Cloud Admin permission set is not directly relevant to creating or executing prompt templates but is more focused on managing the Data Cloud.

* 'Permissions and Access for Prompt Templates | Salesforce Trailhead' .