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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Trust Layer & Security | - Data Masking
| |
| Topic 2: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 3: Agent Force Concepts | 30% | - Einstein GPT Integration
|
| Topic 4: Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
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NEW QUESTION # 229
Universal Containers has PDF maintenance guides in an external folder, not yet in Salesforce.
The team wants a standard, clicks-only setup for the Service Agent to use these documents.
Which approach should the Agentforce Specialist implement?
Answer: A
Explanation:
According to the AgentForce Data Library and Retrieval Configuration Guide, when organizations have external PDF or text documents that need to be used by an AI agent, the recommended clicks-only approach is to upload the documents as a File Source in the AgentForce Data Library.
The system automatically processes the uploaded files, chunks their content, builds a Search Index, and allows you to create a retriever to ground agent responses from those indexed documents.
This method requires no code or manual integration and ensures that all document content becomes queryable through retrieval-augmented generation (RAG).
NEW QUESTION # 230
Universal Containers (UC) is preparing to use the Agentforce Testing Center to ensure the reliability of a new agent. UC has a CSV file with test cases and is reviewing the documentation to understand best practices and limitations. Which best practice should the company follow to avoid modifying CRM data while running tests in the Testing Center?
Answer: B
Explanation:
According to the AgentForce Testing and Validation Guidelines, all automated or large-scale test runs in Testing Center should be executed in a sandbox environment to prevent any unintended modifications to live CRM data.
Running tests in production can trigger record updates, create cases, or call actions that alter live data, violating best practice standards for safe validation. Testing in a sandbox ensures the environment mirrors production logic while maintaining data isolation.
NEW QUESTION # 231
Universal Containers (UC) has recently received an increased number of support cases. As a result, UC has hired more customer support reps and has started to assign some of the ongoing cases to newer reps. Which generative AI solution should the new support reps use to understand the details of a case without reading through each case comment?
Answer: C
Explanation:
New customer support reps at Universal Containers can use Einstein Work Summaries to quickly understand the details of a case without reading through each case comment. Work Summaries leverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.
Agent can assist with a variety of tasks but is not specifically designed for summarizing case details.
Einstein Sales Summaries are focused on summarizing sales-related activities, which is not applicable for support cases.
NEW QUESTION # 232
Universal Containers implements Custom Agent Actions to enhance its customer service operations. The development team needs to understand the core components of a Custom Agent Action to ensure proper configuration and functionality. What should the development team review in the Custom Agent Action configuration to identify one of the core components of a Custom Agent Action?
Answer: A
Explanation:
UC's development team needs to identify a core component of a Custom Agent Action in Agent Builder.
NEW QUESTION # 233
Universal Containers built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors. What is the cause of the random nature of this error?
Answer: A
Explanation:
In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM's token capacity (e.g., input + output tokens). The random nature of these failures suggests variability in the token count across different records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record-some records might have short text fields while others have lengthy ones-the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM's limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.
NEW QUESTION # 234
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