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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply the Trusted AI Principles of Salesforce to given scenarios.
Topic 2	AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.
Topic 3	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Topic 4	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.

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Salesforce Certified AI Associate Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
Cloud Kicks wants to use AI to enhance its sales processesand customer support.
Which capacity should they use?

A. Einstein Lead Scoring and Case Classification
B. Sales path and Automaton Case Escalations
C. Dashboard of Current Leads and Cases

Answer: A

Explanation:
"Einstein Lead Scoring and Case Classification are thecapabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI tooptimize sales and service processes by providing insights and recommendations based ondata. Einstein Lead Scoring can help prioritize leads based on their likelihood to convert, while Einstein Case Classification can help categorize and route cases based on their attributes."

NEW QUESTION # 59
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, with missing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company's operations?

A. The accuracy of product recommendations is hindered.
B. The response time for product recommendations is stalled.
C. The diversity of product recommendations Is Improved.

Answer: A

Explanation:
Explanation
"The incomplete data quality will impact the company's operations by hindering the accuracy of product recommendations. Incomplete data means that the data is missing some values or attributes that are relevant for the AI task. Incomplete data can affect the performance and reliability of AI models, as they may not have enough information to learn from or make accurate predictions. For example, incomplete customer records can affect the quality of product recommendations, as the AI model may not be able to capture the customers' preferences, behavior, or needs."

NEW QUESTION # 60
Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.
Which type of bias is most likely to be encountered in this scenario?

A. Confirmation
B. Societal
C. Survivorship

Answer: A

Explanation:
"Confirmation bias is most likely to be encountered in this scenario. Confirmation bias is a type of bias that occurs when data or information confirms or supports one'sexisting beliefs or expectations. For example, confirmation bias can occur when a product recommendation feature only recommends shoes of a given color based on the customer's purchase history, without considering other factors or preferences that may influence their choice."

NEW QUESTION # 61
What are some key benefits of AI in improving customer experiences in CRM?

A. Fully automates the customer service experience, ensuring seamless automated interactions with customers
B. Streamlines case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions
C. Improves CRM security protocols, safeguarding sensitive customer data from potential breaches and threats

Answer: B

NEW QUESTION # 62
Cloud Kicks wants to use an AI mode to predict the demand for shoes using historical data on sales and regional characteristics.
What is an essential data quality dimension to achieve this goal?

A. Volume
B. Reliability
C. Age

Answer: B

Explanation:
Explanation
"Reliability is an essential data quality dimension to achieve the goal of predicting the demand for shoes using historical data on sales and regional characteristics. Reliability means that the data values are trustworthy, credible, and authoritative for the AI task. Reliable data can improve the accuracy and confidence of AI predictions, as they reflect the true state or condition of the target population or domain. For example, reliable data can help predict the demand for shoes by using verified and validated sales and regional data."

NEW QUESTION # 63
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