Agentforce-Specialist Practice Questions

A.  

Create an agent action referencing the password reset Flow, assign it to the identity verification subagent, and call it deterministically from the subagent’s reasoning block using Agent Script conditional logic.

B.  

Create a separate subagent named Password Reset, configure it with an action that invokes the Flow, and pass the identity verification variable as context so the subagent’s instructions can reference the verified status before proceeding.

C.  

Add an instruction to the identity verification subagent directing the agent to trigger the password reset Flow once identity is confirmed, and store the customer’s reset intent in a conversation variable so the reasoning engine can reference it when deciding whether to proceed.

A.  

Agent Action: Find Similar Opportunities

B.  

Agent Action: Draft or Revise Sales Email

C.  

Agent Action: Summarize Record

A.  

Execute a flow as an agent action to calculate the fee, and make the flow’s output directly available to the agent response. The agent will be able to continue to use this value from memory

B.  

Define a context variable for the fee. Execute a flow as an agent action to calculate the fee, assign the flow’s output to that context variable, and have the agent reference that variable in its responses

C.  

Provide the mathematical formula for the restocking fee in the agent’s system instructions and instruct it to remember the result for reuse

A.  

After a prompt template version is activated, no further changes can be saved to that version.

B.  

Only the latest version of a template can be activated.

C.  

Every modification on a template will be saved as a new version automatically.

A.  

Agent-to-Agent (A2A) protocol support

B.  

Model Context Protocol (MCP) server support

C.  

MuleSoft connector for custom hosted processes

A.  

Use chain-of-thought reasoning.

B.  

Define the desired output structure with explicit headings in the instruction.

C.  

Use a prompt template-triggered flow to format responses.

A.  

Configure the prompt template tone with a dataset of past interactions using different writing styles, intensifiers, and punctuation to permanently alter the LLM default tone.

B.  

Include a direct instruction asking the LLM to role-play as a specific character, for example, “Act as an empathetic customer support agent,” to provide context and establish the tone.

C.  

Include multiple-choice picklist questions within the prompt template to systematically test and correct the LLM’s understanding of the desired context before generating the output.

A.  

Incorporate open-ended questions to encourage detailed responses

B.  

Provide clear, positive instructions and use few-shot examples

C.  

Use a high temperature setting to increase creative output

A.  

Role: You are an experienced support agent. Task: Summarize the case notes, Context: Include customer issue, troubleshooting steps, and next actions. Constraints: Limit to 5 sentences, use plain language, and if no next action is found, state “No next action

B.  

Role: You are a support agent writing a case summary. Task: Provide a professional summary of the issue and troubleshooting steps. Contest: Include customer issue, steps taken, and next actions if available. Constraints: No strict sentence limit, but use plain language. If no next action is found, leave it out. Format: Use paragraphs for readability.

C.  

Role: You are a case documentation assistant, Task: Write a summary of the support call. Context: Always describe the customer issue, troubleshooting, and resolution details. Constraints: The summary should be comprehensive and professional, but there is no limit on length or language style. Format: Use complete sentences in a narrative style.

A.  

Run a report on the Platform Debug Logs.

B.  

Query the Agent log data using the Metadata API.

C.  

Run Agent Analytics.