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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Connecting external data sources 2. Using Data Cloud for retrieval augmentation
Topic 2: Governance & Observability	10%	- AI governance 1. Monitoring agent behavior 2. Trust layer and compliance concepts
Topic 3: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
Topic 4: Prompt Engineering	20%	- Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations - Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration
Topic 5: AI Agents	35%	- Agent behavior configuration 1. Filters and variables for deterministic behavior 2. Agent reasoning engine concepts - Agent setup and orchestration 1. Standard and custom agent actions 2. Standard and custom topics

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Salesforce Certified Agentforce Specialist Sample Questions (Q179-Q184):

NEW QUESTION # 179
Universal Containers (UC) configured a new PDF file ingestion in Data Cloud with all the required fields, and also created the mapping and the search Index. UC Is now setting up the retriever and notices a required fleld is missing. How should UC resolve this?

A. Update the search index to include the desired field.
B. Modify the retriever's configuration to include the desired field..
C. Create a new custom Data Cloud object that includes the desired field.

Answer: A

Explanation:
Why is "Update the search index to include the desired field" the correct answer?
When configuring a retriever in Data Cloud for PDF file ingestion, all necessary fields must be included in the search index. If a required field is missing, the correct action is to update the search index to ensure it is available for retrieval.
Key Considerations for Fixing Missing Fields in Data Cloud Retrievers:
Search Index Controls Which Fields Are Searchable
The search index defines which fields are indexed and accessible to the retriever.
If a field is missing, it must be added to the index before it can be queried.
Ensures Complete and Accurate Data Retrieval
Without indexing, the retriever cannot reference the missing field in AI responses.
Updating the index makes the field available for AI-powered retrieval.
Supports AI-Grounded Responses
Agentforce relies on Retriever-Augmented Generation (RAG) to ground AI responses in searchable Data Cloud content.
Ensuring all relevant fields are indexed improves AI-generated answer accuracy.

NEW QUESTION # 180
Before activating a custom Agent action, an Agentforce Specialist would like to understand multiple real-world user utterances to ensure the action is being selected appropriately. Which tool should the Specialist recommend?

A. Model Playground
B. Agent Builder
C. Agentforce

Answer: A

NEW QUESTION # 181
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions. Which use case functionality of Model Builder aligns with UC's request?

A. Contract Renewal Date prediction
B. Product recommendation prediction
C. Customer churn prediction

Answer: C

Explanation:
Customer churn prediction is the best use case for Model Builder in addressing Universal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn, UC can proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.

NEW QUESTION # 182
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?

A. Create a prompt template that takes a list of all Data Cloud activity records as input to pass to the large language model (LLM).
B. Call the prompt directly from Data Cloud with a web tracing activity included in the prompt definition.
C. Add the activity records as an enrichment related list to the Contact then pass the Contact into a prompt template workspace using related list grounding.

Answer: C

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 # 183
Universal Containers (UC) currently tracks Leads with a custom object. UC is preparing to implement the Sales Development Representative (SDR) Agent. Which consideration should UC keep in mind?

A. Agentforce SDR only supports custom objects associated with Accounts.
B. Agentforce SDR only works on Opportunities.
C. Agentforce SDR only works with the standard Lead object.

Answer: C

Explanation:
Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent designed to assist sales teams by qualifying leads and scheduling meetings. Let's evaluate the options based on its functionality and limitations.

NEW QUESTION # 184
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