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Salesforce Certified AI Specialist Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?

A. Predict most popular products from new product catalog.
B. Predict customer sentiment toward a promotion message.
C. Predict customer lifetime value of an account.

Answer: C

Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.
Reference:
Salesforce Einstein Studio Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview

NEW QUESTION # 94
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?

A. Service AI Grounding only works with Case and Knowledge objects.
B. Service AI Grounding visibility works m system mode.
C. Service AI Grounding only supports String and Text Area type fields.

Answer: C

Explanation:
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses. Key considerations:
* Field Types: Standard Service AI Grounding supports String and Text Area fields. Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.
* Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.
* Visibility: Service AI Grounding respects user permissions and sharing settings unless overridden (Option C is incorrect).
References:
* Salesforce Help: Service AI Grounding Requirements
* Explicitly states support for "Text Area and String fields" only.

NEW QUESTION # 95
Universal Containers (UC) wants to improve the efficiency of addressing customer questions and reduce agent handling time with AI- generated responses. The agents should be able to leverage their existing knowledge base and identify whether the responses are coming from the large language model (LLM) or from Salesforce Knowledge.
Which step should UC take to meet this requirement?

A. Turn on Service AI Grounding and Grounding with Knowledge.
B. Turn on Service Replies, Service AI Grounding, and Grounding with Knowledge.
C. Turn on Service AI Grounding, Grounding with Case, and Service Replies.

Answer: A

Explanation:
To meet Universal Containers' goal of improving efficiency and reducing agent handling time with AI- generated responses, the best approach is to enable Service Replies, Service AI Grounding, and Grounding with Knowledge.
* Service Replies generates responses automatically.
* Service AI Grounding ensures that the AI is using relevant case data.
* Grounding with Knowledge ensures that responses are backed by Salesforce Knowledge articles, allowing agents to identify whether a response is coming from the LLM or Salesforce Knowledge.
* Option C does not include Service Replies, which is necessary for generating AI responses.
* Option A lacks the Grounding with Knowledge, which is essential for identifying response sources.
For more details, refer to Salesforce Service AI documentation on grounding and service replies.

NEW QUESTION # 96
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.

Answer: 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.

NEW QUESTION # 97
Universal Containers is considering leveraging the Einstein Trust Layer in conjunction with Einstein Generative AI Audit Data.
Which audit data is available using the Einstein Trust Layer?

A. Response accuracy and offensiveness score
B. Hallucination score and bias score
C. Masked data and toxicity score

Answer: C

Explanation:
Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.
The audit data available through the Einstein Trust Layer includes information about masked data-which ensures sensitive information is not exposed-and the toxicity score, which evaluates the generated content for inappropriate or harmful language.
Reference:
Salesforce AI Specialist Documentation - Einstein Trust Layer: Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.

NEW QUESTION # 98
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