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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 topics 2. Standard and custom agent actions
Topic 2: Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
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: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
Topic 5: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Connecting external data sources 2. Using Data Cloud for retrieval augmentation

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AI-201 Exam, Latest AI-201 Exam Topics

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Salesforce Certified Agentforce Specialist Sample Questions (Q36-Q41):

NEW QUESTION # 36
Support agents at Universal Containers are using Agentforce to find troubleshooting information.
They've reported that the agent frequently provides knowledge articles that are outdated, even when newer versions of the articles are available. The administrator has confirmed that all articles are correctly chunked and indexed. Which configuration change in the Data Cloud hybrid search index best addresses this problem?

A. Disable the keyword index to rely solely on the vector index.
B. Add a ranking factor for regency based on the LastModifiedDate field.
C. Switch the chunking strategy from section-aware to fixed-size.

Answer: B

Explanation:
The AgentForce Data Cloud Retrieval and Ranking Guide highlights that when outdated Knowledge articles appear before newer ones, administrators should configure ranking factors that prioritize content based on recency. The documentation specifies: "Adding a recency ranking factor using the LastModifiedDate or LastPublishedDate fields ensures the retrieval prioritizes the most up-to-date documents, improving response relevance."

NEW QUESTION # 37
How should an organization use the Einstein Trust layer to audit, track, and view masked data?

A. In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
B. Access the audit trail in Setup and export all user-generated prompts.
C. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.

Answer: C

Explanation:
The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:
Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.

NEW QUESTION # 38
An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the correct order. Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner service to work as expected?

A. Specify the dependent actions with the reference to the action API name.
B. Specify the LLM model provider and version to be used to invoke the action.
C. Specify the profiles or custom permissions allowed to invoke the action.

Answer: A

Explanation:
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, the Agentforce Specialist must explicitly specify dependent actions using their API names in the custom action's configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A's API name must be listed as a dependency in Action B's instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.

NEW QUESTION # 39
Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?

A. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article and select Show sources,
B. Use Data Categories to categorize publicly available articles to index. Sources are automatically displayed when knowledge articles are categorized as Public.
C. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article. It is not possible to display articles that the LLM grounded its response in.

Answer: A

Explanation:
According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:
"Within the data library settings, under Knowledge Settings, enable `Use Public Knowledge Articles' to ensure only publicly visible content is indexed. To display citations, enable `Show Sources' so the agent links the specific articles or data records used to ground its response."

NEW QUESTION # 40
After an agent selects a topic, what is an important factor the reasoning engine uses to select the action?

A. The explicit order of actions in the topic
B. The priority given to each action
C. The name and instructions of the actions

Answer: B

Explanation:
The most crucial factor a reasoning engine uses to select an action after a topic is chosen is the priority given to each action (A). In advanced agent frameworks like AgentForce (simulated context), actions within a topic are typically not executed simply in an explicit, fixed order () unless there's no conditional logic. Instead, the reasoning engine evaluates all available actions and their associated pre-conditions (or triggers) and priorities. A priority score is often a numerical value assigned to an action that dictates its relative importance when multiple actions could potentially be executed simultaneously or when the agent must choose the 'best' action to address the current topic state. This prioritization ensures the agent handles the most critical or relevant tasks first, which is essential for efficient and goal-oriented behavior. The action's name and instructions () are descriptive for the developer but are not the primary selection criteria used by the runtime reasoning engine itself; it's the logic and priority that govern execution.

NEW QUESTION # 41
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