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Salesforce Certified AI Specialist Exam Questions
Certified AI Specialist (Page 3 )

Updated On: 16-Feb-2026
Page 3 of 32
PDF with 152 Questions

An administrator wants to check the response of the Flex prompt template they've built, but the preview button is greyed out.
What is the reason for this?

  1. The records related to the prompt have not been selected.
  2. The prompt has not been saved and activated,
  3. A merge field has not been inserted in the prompt.

Answer(s): A

Explanation:

When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template. Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality. Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.
Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.
Salesforce AI Specialist


Reference:

Refer to the documentation on troubleshooting Flex templates here:
https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm



Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?

  1. Model Builder
  2. App Builder
  3. Copilot Builder

Answer(s): A

Explanation:

To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities. Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce AI Specialist


Reference:

For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm



An AI Specialist built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors.
What is the cause of the random nature of this error?

  1. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
  2. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
  3. The number of tokens that can be processed by the LLM varies with total user demand.

Answer(s): A

Explanation:

The reason behind the token limit errors lies in the dynamic nature of the prompt template used in Field Generation. In Salesforce's AI generative models, each prompt and its corresponding output are subject to a token limit, which encompasses both the input and output of the large language model (LLM). Since the prompt template dynamically adjusts based on the specific data of each record, the number of tokens varies per record. Some records may generate longer outputs based on their data attributes, pushing the token count beyond the allowable limit for the LLM, resulting in token limit errors.
This behavior explains why users experience random failures--it is dependent on the specific data used in each case. For certain records, the combined input and output may fall within the token limit, while for others, it may exceed it. This variation is intrinsic to how dynamic templates interact with large language models.
Salesforce provides guidance in their documentation, stating that prompt template design should take into account token limits and suggests testing with varied records to avoid such random errors. It does not mention switching to Flex template type as a solution, nor does it suggest that token limits fluctuate with user demand. Token limits are a constant defined by the model itself, independent of external user load.


Reference:

Salesforce Developer Documentation on Token Limits for Generative AI Models

Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)



An administrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM dat

  1. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt.
    Which feature in the Einstein Trust Layer best supports UC's need?
  2. Data masking
  3. Dynamic grounding with secure data retrieval
  4. Zero-data retention policy

Answer(s): B

Explanation:

Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI- generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.
Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model's outputs are trustworthy and reliable for business use.
The other options are less aligned with the requirement:
Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.
Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.


Reference:

Salesforce Developer Documentation on Einstein Trust Layer Salesforce Security Documentation for AI and Data Privacy



A Salesforce Administrator is exploring the capabilities of Einstein Copilot to enhance user interaction within their organization. They are particularly interested in how Einstein Copilot processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Einstein Copilot directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users. How does Einstein Copilot handle user requests In Salesforce?

  1. Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.
  2. Einstein Copilot will perform an HTTP callout to an LLM provider.
  3. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.

Answer(s): C

Explanation:

Einstein Copilot is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries.
When a user submits a request, Einstein Copilot analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.
Option C accurately describes this process. Einstein Copilot does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range of user requests.


Reference:

Salesforce AI Specialist Documentation - Einstein Copilot Overview: Details how Einstein Copilot employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem. Salesforce Help - How Einstein Copilot Works: Explains the underlying mechanisms of how Einstein Copilot processes user requests using AI technologies.






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