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The Salesforce world has become so competitive and challenging. To say updated and meet the challenges of the market you have to learn new in-demand skills and upgrade your knowledge. With the Salesforce AI-201 Certification Exam everyone can do this job nicely and quickly. The Salesforce Certified Agentforce Specialist (AI-201) certification exam offers a great opportunity to validate the skills and knowledge.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt management and user roles - Prompt Builder usage and use cases - Grounding techniques and best practices |
| Topic 2: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding |
| Topic 3: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production - Version control and maintenance |
| Topic 4: AI Agents | 35% | - Topics and actions configuration - Agent types: Service, Sales, Employee - Agent User security and permissions - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack |
| Topic 5: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
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NEW QUESTION # 223
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio. Which use case works for this scenario?
Answer: B
Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
NEW QUESTION # 224
Universal Containers deploys a new Agentforce Service Agent into the company's website but is getting feedback that the Agentforce Service Agent is not providing answers to customer questions that are found in the company's Salesforce Knowledge articles. What is the likely issue?
Answer: B
Explanation:
Universal Containers (UC) has deployed an Agentforce Service Agent on its website, but it's failing to provide answers from Salesforce Knowledge articles.
NEW QUESTION # 225
An Al Specialist is tasked with creating a prompt template for a sales team. The template needs to generate a summary of all related opportunities for a given Account. Which grounding technique should the Al Specialist use to include data from the related list of opportunities in the prompt template?
Answer: B
Explanation:
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
NEW QUESTION # 226
An Agentforce configured Data Masking within the Einstein Trust Layer. How should the Agentforce Specialist begin validating that the correct fields are being masked?
Answer: B
Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the Agentforce Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the Agentforce Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
NEW QUESTION # 227
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
Answer: A
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data.
The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI- powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions. Big Objects are more suited for archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis capabilities.
NEW QUESTION # 228
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