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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
Topic 2: Governance & Observability	10%	- AI governance 1. Monitoring agent behavior 2. Trust layer and compliance concepts
Topic 3: Prompt Engineering	20%	- Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration - Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations
Topic 4: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources
Topic 5: AI Agents	35%	- Agent behavior configuration 1. Agent reasoning engine concepts 2. Filters and variables for deterministic behavior - 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 (Q285-Q290):

NEW QUESTION # 285
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts. Which approach should the Agentforce Specialist recommend to achieve this use case?

A. Deploy UC's own custom foundational model on this data first.
B. Fine-Tune the standard foundational model due to the complexity of the data.
C. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.

Answer: C

Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.

NEW QUESTION # 286
An administrator at Universal Containers has successfully deployed a new agent from a sandbox to production using a change set. The agent uses a prompt template that invokes a Salesforce flow to perform a complex calculation. In production, when users interact with the agent, it fails with an error message every time the flow is supposed to run. The flow was included in the change set and is present in production. What is the most likely cause of this issue?

A. The flow was not manually activated in the production org after the deployment.
B. The user in production does not have permission to run the flow.
C. The change set did not include the dependent Apex classes for the flow.

Answer: A

Explanation:
Per the AgentForce Deployment and Flow Integration Guide, when deploying flows via change sets, the flows arrive in the production org in "inactive" status by default. The administrator must manually activate the flow post-deployment before it can be executed by agents or users.
This explains why the agent encounters an error when attempting to run the flow--the system recognizes the flow but cannot invoke it because it remains inactive.

NEW QUESTION # 287
Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases. UC is considering implementing Agent and extending Service Cloud to mobile users. When would Agent implementation be most advantageous?

A. When the goal is to streamline customer support processes and improve response times
B. When the main objective is to enhance data security and compliance measures
C. When the focus is on optimizing marketing campaigns and strategies

Answer: A

Explanation:
Agent implementation would be most advantageous in Salesforce Service Cloud when the goal is to streamline customer support processes and improve response times. Agent can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.

NEW QUESTION # 288
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?

A. The prompt template version is incompatible with the chosen LLM.
B. The data being used for grounding is incorrect or incomplete.
C. The Einstein Trust Layer is incorrectly configured.

Answer: B

Explanation:
UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause.

NEW QUESTION # 289
Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the "Save As" function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters. What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?

A. Use Model Playground to create a model configuration with the specified parameters.
B. Manually add the hyperparameters to the new template.
C. Revert to using the standard template without modifications.

Answer: B

Explanation:
When Universal Containers creates a new Sales Email prompt template using the "Save As" function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the Agentforce Specialist should manually add the necessary hyperparameters to the new template.
Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty directly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.

NEW QUESTION # 290
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