Question 1

After configuring and saving a Salesforce Agentforce Data Library (regardless of the data source), which components are automatically created and available in Data Cloud?

Options :

A : A data pipeline, an indexing engine, and a query processor

B : A data connector, an analytics dashboard, and a workflow rule

C : A data stream, a search index, and a retriever

Answer: C

Question 2

Universal Containers recently added a custom flow for processing returns and created a new Agent Action. Which action should the company take to ensure the Agentforce Service Agent can run this new flow as part of the new Agent Action?

Options :

A : Recreate the flow using the Agentforce agent user.

B : Assign the Manage Users permission to the Agentforce Agent user.

C : Assign the Run Flows permission to the Agentforce Agent user.

Answer: C

Question 3

Choose 1 option.

Coral Cloud Resorts is uploading thousands of new HTML knowledge articles files for a resort launch.

To ensure Agentforce retrieves accurate responses quickly, which chunking strategy should be used when

creating a new index?

Options :

A : Semantic-based passage extraction

B : Conversation-based chunking

C : Section-aware chunking

Answer: C

Question 4

Universal Containers (UC) needs to capture and store detailed interaction data for all agents.

Which feature should help UC get a full view of the agent's behavior from start to finish, including reasoning

engine executions,

actions, prompt and gateway inputs/outputs, error messages, and final responses?

Options :

A : Agentforce Analytics

B : Utterance Analysis

C : Agentforce Session Tracing

Answer: C

Question 5

A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO). How should the Agentforce Specialist set this up?

Options :

A : Create the serving endpoint in Databricks, then configure the model using Model Builder.

B : Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.

C : Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.

Answer: A

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