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Salesforce Certified Agentforce Specialist Sample Questions (Q157-Q162):

NEW QUESTION # 157
For an Agentforce Data Library that contains uploaded files, what occurs once it is created and configured?

A. Indexes the uploaded files in Salesforce File Storage
B. Indexes the uploaded files in a location specified by the user
C. Indexes the uploaded files into Data Cloud

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, aData Libraryis a feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven agents. Once the Data Library is created and configured, the uploaded files areindexedto make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key question is where this indexing occurs. Salesforce Agentforce integrates tightly withData Cloud, a unified data platform that includes a vector database optimized for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud's vector database, enabling the AI to efficiently retrieve relevant information from them during conversations or actions.
* Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.
* Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its vector database to support AI capabilities like semantic search and content retrieval.
* Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for indexing, not basic file storage.
Thus, Option B accurately reflects the process after a Data Library is created and configured in Agentforce.
References:
* 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: "Vector Database for AI" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_vector_database.htm&type=5)

NEW QUESTION # 158
Universal Containers (UC) plans to automatically populate the Description field on the Account object.
Which type of prompt template should UC use?

A. Sales Email prompt template
B. Field Generation prompt template
C. Flex Prompt template

Answer: B

Explanation:
* Context of the QuestionUniversal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.
* Field Generation Prompt Template
* Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.
* Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.
* Why Not Flex or Sales Email Prompt Templates?
* Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.
* Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting outreach or follow-up messages). This template is not specifically built to populate a field on a record.
* ConclusionFor automatically populating the Description field with AI-generated content, theField Generation prompt template(Option A) is the correct choice.
SalesforceAgentforce SpecialistReferences & Documents
* Salesforce Documentation:Prompt Template TypesExplains various template types (Field Generation, Flex, Email, etc.) and their typical use cases.
* SalesforceAgentforce SpecialistStudy GuideHighlights Field Generation prompt templates for populating or updating record fields with AI-generated text.

NEW QUESTION # 159
Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Agentforce Agents. How should the Agentforce Specialist monitor Agents' usability and the assignment of actions?

A. Query the Agent log data using the Metadata API.
B. Run a report on the Platform Debug Logs.
C. Run Agent Analytics.

Answer: C

Explanation:
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 providesAgent 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.
References:
* 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)

NEW QUESTION # 160
Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?

A. Case Summaries
B. Case Escalation
C. Service Replies

Answer: C

Explanation:
* Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.
* Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.
* Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.
Thus,Service Repliesis the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.
Reference:
"Boost Productivity with Generative AI in Service Cloud | Salesforce Trailhead" .

NEW QUESTION # 161
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should An Agentforce recommend to resolve the issue?

A. Configure data masking in the Einstein Trust Layer setup.
B. Add new data masking rules in LLM setup.
C. Enable data masking for sandbox refreshes.

Answer: A

Explanation:
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the Agentforce Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
* Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
* Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce Agentforce Specialist References:
For more information, refer to: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_data_masking.htm

NEW QUESTION # 162
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