Practice Free Salesforce Agentforce Specialist Exam Online Questions

Question #179

What is a unique characteristic of CSMA/CA?

A . The AP uses a point coordination function to instruct the stations when to send.
B . CSMA/CA is able to detect collisions after transmission.
C . CSMA/CA avoids collisions by first asking permission of the AP to send.
D . Every frame must be acknowledged by the receiving station.

Question #2

Coral Cloud Resorts wants to handle frequent customer misspellings of package names in queries.

Which approach should the Agentforce Specialist implement?

A . Hybrid search
B . Vector search
C . Keyword search

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Question #3

Universal Containers (UC) wants to enable its sales team to use Al to suggest recommended products from its catalog.

Which type of prompt template should UC use?

A . Record summary prompt template
B . Email generation prompt template
C . Flex prompt template

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Question #4

Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.

What is a consideration for this requirement?

A . Storing this data requires Data Cloud to be provisioned.
B . Storing this data requires a custom object for data to be configured.
C . Storing this data requires Salesforce big objects.

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Question #5

An Agentforce Specialist is creating a custom action in Agentforce.

Which option is available for the Agentforce Specialist to choose for the custom Agent action?

A . Apex Trigger
B . SOQL
C . Flows

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Question #6

Cloud Kicks wants its Agentforce Service Agent to retrieve up-to-date factual data from the internet.

Which step must an Agentforce Specialist take to ensure the agent uses the web search capability properly?

A . Enable the Answer Questions with Knowledge standard action specifically on the General FAQ subagent.
B . Map the web search retriever to a custom search index in Data 360 before activating the agent.
C . Remove the default General FAQ subagent and replace its function with a new General Web Search subagent.

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Question #7

Universal Containers’ agent must always look up the customer’s account tier and open cases from Salesforce before deciding how to respond.

Based on Agent Script flow of control, what is true about executing deterministic actions at the very start of a subagent?

A . Actions can only be guaranteed to run by placing them in the config block.
B . Only before_reasoning can guarantee the large language model (LLM) is invoked before an action runs.
C . The first instruction in reasoning.instructions always runs before the large language model (LLM) is invoked.

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Question #8

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.

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Correct Answer: B
B

Explanation:

UC’s custom prompt for summarizing case records is underperforming, and we need to identify a likely cause. Let’s evaluate the options based on Agentforce and Einstein Generative AI mechanics.

Option A: The prompt template version is incompatible with the chosen LLM.

Prompt templates in Agentforce are designed to work with the Atlas Reasoning Engine, which abstracts the underlying large language model (LLM). Salesforce manages compatibility between prompt templates and LLMs, and there’s no user-facing versioning that directly ties to LLM compatibility. This option is unlikely and not a common issue per documentation.

Option B: The data being used for grounding is incorrect or incomplete.

Grounding is the process of providing context (e.g., case record data) to the AI via prompt templates. If the grounding data―sourced from Record Snapshots, Data Cloud, or other integrations―is incorrect (e.g., wrong fields mapped) or incomplete (e.g., missing key case details), the summaries will be inaccurate. For example, if the prompt relies on Case.Subject but the field is empty or not included, the output will miss critical information. This is a frequent cause of poor performance in

generative AI and aligns with Salesforce troubleshooting guidance, making it the correct answer.

Option C: The Einstein Trust Layer is incorrectly configured.

The Einstein Trust Layer enforces guardrails (e.g., toxicity filtering, data masking) to ensure safe and compliant AI outputs. Misconfiguration might block content or alter tone, but it’s unlikely to cause summaries to lack appropriate information unless specific fields are masked unnecessarily. This is less probable than grounding issues and not a primary explanation here.

Why Option B is Correct:

Incorrect or incomplete grounding data is a well-documented reason for subpar AI outputs in Agentforce. It directly affects the quality of case summaries, and specialists are advised to verify grounding sources (e.g., field mappings, Data Cloud queries) when troubleshooting, as per official guidelines.

Reference: Salesforce Agentforce Documentation: Prompt Templates > Grounding C Links poor outputs to grounding issues.

Trailhead: Troubleshoot Agentforce Prompts C Lists incomplete data as a common problem.

Salesforce Help: Einstein Generative AI > Debugging Prompts C Recommends checking grounding data first.

Question #8

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.

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Question #10

Universal Containers wants to assign agents to improve department efficiency.

Which configuration ensures the right tasks are handled by the right agents?

A . SDR Agent for lead qualification, Service Agent for support tickets, Employee Agent for HR requests
B . Sales Coach Agent for lead and service Agent for HR requests, and Support tickets to ensure cases are available
C . One Service Agent to efficiently handle each of these scenarios, which reduces the number of agent types needed for support

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