AI Specialist Agentforce-Specialist Book

As per the AgentForce Deployment and Lifecycle Management Guide, AgentForce agents, including their topics, actions, and prompt templates, can be deployed from sandbox to production using Salesforce change sets.

Administrators should create an outbound change set in the sandbox environment that includes all relevant components (Agent definition, prompt templates, topic configurations, flows, and permissions) and then upload it to production for validation and deployment. This process ensures consistency, auditability, and proper dependency tracking between environments.

Option A is incorrect because manual recreation is inefficient and error-prone. Option B is not recommended, as JSON export/import is for development backups and advanced metadata operations, not for standard deployments.

Hence, the correct approach per AgentForce’s best practices is Option C – Create an outbound change set and deploy it to production.

Why is "The data source cannot be changed after it is selected" the correct answer?

When configuring an Agentforce Data Library, the data source selection is permanent. Once a data source is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability within Salesforce's AI-driven environment.

Key Considerations in Agentforce Data Library

Data Source Lock-In

The chosen data source remains fixed to maintain data integrity and avoid inconsistencies.

Any updates or modifications require creating a new Data Library instead of modifying the existing one.

Why Can't the Data Source Be Changed?

The data source defines the foundation of AI-driven workflows, and any modification would disrupt processing logic.

Agentforce tools rely on structured datasets to enable AI-powered recommendations, and changing data sources could lead to inconsistencies in grounding techniques.

Workarounds for Changing Data Sources

If an organization needs to use a different data source, a new Agentforce Data Library must be created and configured from scratch.

Old data can be manually migrated into the new data source for continuity.

Why Not the Other Options?

❌ A. The data source can be changed through the Data Cloud settings.

Incorrect because once the data source is linked to an Agentforce Data Library, it cannot be altered, even via Data Cloud settings.

❌ B. The Data Retriever can be reconfigured to use a different data source.

Incorrect as the Data Retriever works within the constraints of the selected data source and does not provide an option to swap data sources post-selection.

Agentforce Specialist References

The Salesforce AI Specialist Material and Salesforce Instructions for the Certification confirm that once a data source is set for an Agentforce Data Library, it cannot be changed​​.

According to the AgentForce and Einstein Studio Integration Guide, when a business requires custom ranking or filtering logic (such as by product line and recency), the correct solution is to build a custom retriever in Einstein Studio. The documentation describes: “Custom retrievers in Einstein Studio enable configuration of metadata filters (e.g., product line) and custom ranking functions such as recency or relevance scoring. This allows fine-tuned control over retrieval beyond the default retriever’s capabilities.”

Option A, the default retriever, provides general ranking and does not natively apply custom filters. Option C, applying semantic embeddings with default filters, is useful for general search optimization but lacks custom ranking logic.

Therefore, Option B aligns with Salesforce’s prescribed method for fine-tuned retrieval control in enterprise use cases requiring metadata-based and recency ranking.

References (AgentForce Documents / Study Guide):

AgentForce Einstein Studio Guide: “Building Custom Retrievers with Metadata and Ranking”

AgentForce Data Cloud Configuration Notes: “Filtering and Ranking in Custom Retrieval”

AgentForce Study Guide: “Advanced Retrieval Customization in Einstein Studio”

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.”

Option A correctly reflects these two documented configuration steps.

Option B is incorrect because Salesforce explicitly supports source display for transparency through the “Show Sources” setting.

Option C incorrectly assumes that Data Categories control indexing visibility and source linking, which is handled by explicit Knowledge Settings, not categorization.

References (AgentForce Documents / Study Guide):

AgentForce Data Library Configuration Guide: “Knowledge Settings for Public Article Indexing”

AgentForce Transparency and Source Attribution Notes: “Show Sources Option”

AgentForce Study Guide: “Configuring Knowledge Visibility and Source Display”