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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding - Agentforce Data Library types |
| Topic 2: AI Agents | 35% | - Topics and actions configuration - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent User security and permissions - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine |
| Topic 3: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| Topic 4: Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt management and user roles - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types |
| Topic 5: Development Lifecycle | 15% | - Deployment: sandbox to production - Version control and maintenance - Monitoring, adoption and optimization - Testing agents in Testing Center |
>> Latest AI-201 Exam Answers <<
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NEW QUESTION # 314
Coral Cloud Resorts is about to start testing its concierge agent with guests. Which metrics should be captured to monitor the performance, correctness, and user experience?
Answer: A
Explanation:
According to the AgentForce Monitoring and Evaluation Framework, the three key dimensions for measuring AI agent quality are performance, correctness, and user satisfaction. To accurately monitor these, organizations should track:
Response times (to assess system and model latency),
Accuracy and relevance of answers (to measure the grounding and reasoning quality), and Resolution success (to confirm task completion or problem-solving effectiveness).
These metrics provide a balanced evaluation of both technical efficiency and user experience.
NEW QUESTION # 315
Universal Containers has a requirement to provide a sales summary for its sales reps who are using Employee Agents, but they are not happy with the default answer. Which best practice should the AgentForce Specialist recommend?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Prompt Customization and Template Development Guide, when an organization wants to modify or enhance how structured data (like Salesforce records) is summarized or presented by an agent, the correct approach is to create a Record Summary custom prompt template.
Record Summary templates allow specialists to tailor how the agent interprets and communicates record information - such as opportunities, accounts, or sales summaries - ensuring the output aligns with company-specific terminology, tone, and data needs.
NEW QUESTION # 316
What is true of Agentforce Testing Center?
Answer: C
Explanation:
The Agentforce Testing Center is a tool in Agentforce Studio for validating agent performance.
NEW QUESTION # 317
Universal Containers (UC) wants to limit an agent's access to Knowledge articles while deploying the "Answer Questions with Knowledge" action. How should UC achieve this?
Answer: B
Explanation:
UC wants to restrict the "Answer Questions with Knowledge" action to a subset of Knowledge articles.
NEW QUESTION # 318
Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields. Which combination of Agentforce for Service features enables this effort?
Answer: A
Explanation:
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.
NEW QUESTION # 319
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