Practice Free Salesforce AI Specialist Exam Online Questions
An AI Specialist needs to enable the use of Sales Email prompt templates for the sales team. The AI Specialist has already created the templates in Prompt Builder.
According to best practices, which steps should the AI Specialist take to ensure the sales team can use these templates?
- A . Assign the Prompt Template User permission set and enable Sales Emails in Setup.
- B . Assign the Prompt Template Manager permission set and enable Sales Emails in setup.
- C . Assign the Data Cloud Admin permission set and enable Sales Emails in Setup.
A
Explanation:
To enable Sales Email prompt templates:
Permission Set: Assign the Prompt Template User permission set to the sales team to grant access to use pre-built templates.
Feature Activation: Enable Sales Emails in Salesforce Setup to activate the integration between prompt templates and email workflows.
Option B (Manager permission set): Required for creating/modifying templates, not for usage.
Option C (Data Cloud Admin): Unrelated to prompt template access.
Reference: Salesforce Help: Prompt Template Permissions
Specifies that "Prompt Template User" is required to leverage templates in workflows.
Sales Email Setup outlines enabling the feature in Setup.
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature.
What does Einstein consider when generating a summary?
- A . Generation is grounded with conversation context, Knowledge articles, and cases.
- B . Generation is grounded with existing conversation context only.
- C . Generation is grounded with conversation context and Knowledge articles.
A
Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract
relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
Option A is correct as it includes all three: conversation context, Knowledge articles, and cases.
Option B is incorrect because it limits the grounding to conversation context only, excluding other critical elements.
Option C is incorrect because it omits case data, which Einstein considers for more accurate and contextually rich summaries.
Reference: "Einstein Work Summary and AI Case Management | Salesforce Trailhead" .
Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Einstein Copilot.
How should the AI Specialist monitor Copilot’s usability and the assignment of actions?
- A . Run a report on the Platform Debug Logs.
- B . Query the Copilot log data using the metadata API.
- C . Run Einstein Copilot Analytics.
C
Explanation:
To monitor Einstein Copilot’s usability and the assignment of actions, the AI Specialist should run Einstein Copilot Analytics. This feature provides insights into how often Copilot is used, the types of actions it is handling, and overall user engagement with the system. It’s the most effective way to track Copilot’s performance and usage patterns.
Platform Debug Logs are not relevant for tracking user behavior or the assignment of Copilot actions. Querying the Copilot log data via the Metadata API would not provide the necessary insights in a structured manner.
For more details, refer to Salesforce’s Copilot Analytics documentation for tracking AI-driven interactions.
Universal Containers, dealing with a high volume of chat inquiries, implements Einstein Work Summaries to boost productivity.
After an agent-customer conversation, which additional information does Einstein generate and fill, apart from the "summary"’
- A . Sentiment Analysis and Emotion Detection
- B . Draft Survey Request Email
- C . Issue and Revolution
C
Explanation:
Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and supervisors quickly grasp the conversation’s context without reviewing the full transcript. Sentiment Analysis and Emotion Detection (Option A): While Einstein Conversation Insights provides sentiment scores and emotion detection, these are separate from Work Summaries. Work Summaries focus on factual summaries, not sentiment.
Draft Survey Request Email (Option B): Not part of Work Summaries. This would require automation tools like Flow or Email Studio.
Issue and Resolution (Option C): Directly referenced in Salesforce documentation as fields populated
by Einstein Work Summaries.
Reference: Salesforce Help Article: Einstein Work Summaries
Einstein Work Summaries focus on "key details like Issue and Resolution" alongside summaries.
Contrast with Einstein Conversation Insights for sentiment/emotion analysis.
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?
- A . The number of tokens generated by the dynamic nature of the prompt template will vary by record.
- B . The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
- C . The number of tokens that can be processed by the LLM varies with total user demand.
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)
Universal Containers wants to reduce overall agent handling time minimizing the time spent typing
routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields.
Which combination of Einstein for Service features enables this effort?
- A . Einstein Service Replies and Work Summaries
- B . Einstein Reply Recommendations and Case Summaries
- C . Einstein Reply Recommendations and Case Classification
C
Explanation:
Universal Containers aims to reduce overall agent handling time by minimizing the time agents spend typing routine answers for common questions during chats and by reducing post-chat analysis through suggesting values for case fields.
To achieve these objectives, the combination of Einstein Reply Recommendations and Case Classification is the most appropriate solution.
Universal Containers wants to reduce overall agent handling time minimizing the time spent typing
routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields.
Which combination of Einstein for Service features enables this effort?
- A . Einstein Service Replies and Work Summaries
- B . Einstein Reply Recommendations and Case Summaries
- C . Einstein Reply Recommendations and Case Classification
C
Explanation:
Universal Containers aims to reduce overall agent handling time by minimizing the time agents spend typing routine answers for common questions during chats and by reducing post-chat analysis through suggesting values for case fields.
To achieve these objectives, the combination of Einstein Reply Recommendations and Case Classification is the most appropriate solution.
An AI Specialist at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team’s time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the AI Specialist recommend to achieve this use case?
- A . Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
- B . Fine-Tune the standard foundational model due to the complexity of the data.
- C . Deploy UC’s own custom foundational model on this data first.
A
Explanation:
For no-code implementations, Prompt Builder allows AI Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
Reference: Salesforce Help Article: Prompt Builder for No-Code AI ("Grounding in CRM and Data Cloud" section).
Einstein GPT Implementation Guide: "Generating Summaries with Pre-Built Models."
Universal Containers (UC) has recently received an increased number of support cases. As a result, UC has hired more customer support reps and has started to assign some of the ongoing cases to newer reps.
Which generative AI solution should the new support reps use to understand the details of a case without reading through each case comment?
- A . Einstein Copilot
- B . Einstein Sales Summaries
- C . Einstein Work Summaries
C
Explanation:
New customer support reps at Universal Containers can use Einstein Work Summaries to quickly understand the details of a case without reading through each case comment. Work Summaries leverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.
Einstein Copilot can assist with a variety of tasks but is not specifically designed for summarizing case details.
Einstein Sales Summaries are focused on summarizing sales-related activities, which is not applicable for support cases.
For more details, refer to Salesforce documentation on Einstein Work Summaries.
Universal Containers wants to make a sales proposal and directly use data from multiple unrelated objects (standard and custom) in a prompt template.
What should the AI Specialist recommend?
- A . Create a Flex template to add resources with standard and custom objects as inputs.
- B . Create a prompt template passing in a special custom object that connects the records temporarily,
- C . Create a prompt template-triggered flow to access the data from standard and custom objects.
A
Explanation:
Universal Containers needs to generate a sales proposal using data from multiple unrelated standard and custom objects within a prompt template. The most effective way to achieve this is by using a Flex template.
Flex templates in Salesforce allow AI specialists to create prompt templates that can accept inputs
from multiple sources, including various standard and custom objects. This flexibility enables the
direct use of data from unrelated objects without the need to create intermediary custom objects or
complex flows.
Reference: Salesforce AI Specialist Documentation – Flex Templates: Explains how Flex templates can be utilized to incorporate data from multiple sources, providing a flexible solution for complex data
requirements in prompt templates.