Latest AI-201 Study Materials | AI-201 New Cram Materials

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Salesforce AI-201 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Cloud for Agentforce20%- Improving response accuracy with data grounding
- Retrievers and search types: keyword, vector, hybrid
- Unstructured data processing: chunking, indexing
- Agentforce Data Library types
Topic 2: Multi-Agent Interoperability5%- Agent API usage scenarios
- Agent-to-agent communication
- Model Context Protocol (MCP)
Topic 3: Prompt Engineering20%- Prompt template creation: field generation, flex types
- Grounding techniques and best practices
- Prompt management and user roles
- Prompt Builder usage and use cases
Topic 4: AI Agents35%- Deterministic controls: filters and variables
- Topics and actions configuration
- Agent architecture and reasoning engine
- Agent User security and permissions
- Channel integration: digital experience, email, Slack
- Agent types: Service, Sales, Employee
Topic 5: Development Lifecycle15%- Monitoring, adoption and optimization
- Version control and maintenance
- Testing agents in Testing Center
- Deployment: sandbox to production

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Salesforce Certified Agentforce Specialist Sample Questions (Q70-Q75):

NEW QUESTION # 70
What is a Salesforce Agentforce Specialist able to configure in Data Masking within the Einstein Trust Layer?

Answer: A

Explanation:
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.
Data Masking Configuration:
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.
This is done through declarative settings in Salesforce, where entities (standard or custom fields) are flagged for masking.


NEW QUESTION # 71
Universal Containers wants to incorporate CRM data as well-formatted JSON in a prompt to a large language model (LLM). What is an important consideration for this requirement?

Answer: C

Explanation:
Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large language model (LLM).
The question is about an important technical or design consideration for including CRM data as JSON in that prompt.


NEW QUESTION # 72
What is the main purpose of Prompt Builder?

Answer: B

Explanation:
Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.


NEW QUESTION # 73
Universal Containers (UC) is tracking web activities in Data Cloud for a unified contact, and wants to use that in a prompt template to help extract insights from the data. Assuming that the Contact object is one of the objects associated with the prompt template, what is a valid way for DC to do this?

Answer: A

Explanation:
To integrate web activity data from Data Cloud into a prompt template, the correct approach is to enrich the Contact object with the activity records as a related list and use related list grounding (Option B). Here's why:
Data Cloud Integration: Data Cloud unifies web activity data and associates it with the unified Contact record. By adding these activities as a related list to the Contact, the data becomes accessible to the prompt template.
Prompt Template Grounding: Salesforce prompt templates support grounding on related records.
When the Contact is passed to the prompt template, the template can reference the related web activity records (via the related list) to extract insights.
Structured Data Handling: This method aligns with Salesforce best practices for grounding, ensuring the large language model (LLM) receives structured, context-rich data without overwhelming it with raw activity lists.


NEW QUESTION # 74
Universal Containers (UC) wants to enable its sales team with automatic post-call visibility into mention of competitors, products, and other custom phrases. Which feature should the Agentforce Specialist set up to enable UC's sales team?

Answer: C

Explanation:
To enable Universal Containers' sales team with automatic post-call visibility into mentions of competitors, products, and custom phrases, the Agentforce Specialist should set up Call Insights.
Call Insights analyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.
Call Summaries provide a general overview of the call but do not specifically highlight keywords or topics.
Call Explorer is a tool for navigating through call data but does not focus on automatic insights.


NEW QUESTION # 75
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