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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:
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Salesforce Certified AI Associate Exam Sample Questions (Q20-Q25):
NEW QUESTION # 20
Which statement exemplifies Salesforces honesty guideline when training AI models?
Answer: A
Explanation:
"Ensuring appropriate consent and transparency when using AI-generated responses is a statement that exemplifies Salesforce's honesty guideline when training AI models. Salesforce's honesty guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for honesty and integrity in how they work and what they produce. Ensuring appropriate consent and transparency means respecting and honoring the choices andpreferences of users regarding how their data is used or generated by AI systems. Ensuring appropriate consent and transparency also means providing clear and accurate information and documentation about the AI systems and their outputs."
NEW QUESTION # 21
Cloud Kicks wants to develop a solutionto predict customers product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.
Which data quality dimension is affected in this scenario?
Answer: C
Explanation:
"Consistency is the data quality dimension that is affected in this scenario. Consistency means that the data values are uniform and follow a common standard or format acrossdifferent records, fields, or sources.
Inconsistent data can cause confusion, errors, or duplication in data analysis and processing. For example, using different field types for the same attribute can affect the consistency of the data."
NEW QUESTION # 22
Cloud Kicks wants to optimize its business operations by incorporating AI into its CRM.
What should the company do first to prepare its data for use with AI?
Answer: C
Explanation:
Before using AI to optimize business operations, the company should first assess the availability and quality of its data. Data is thefuel for AI, and without sufficient and relevant data, AI cannot produce accurate and reliable results. Therefore, the company should identify what data it has, where it is stored, how it is accessed, and how it is maintained. This will help the company understand the feasibility and scope of its AI projects.
NEW QUESTION # 23
What are the potential consequences of an organization suffering from poor data quality?
Answer: B
Explanation:
The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision- making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation.
Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
NEW QUESTION # 24
What are the three commonly used examples of AI in CRM?
Answer: A
Explanation:
"Predictive scoring, forecasting, and recommendations are three commonly used examples of AI in CRM.
Predictive scoring can help prioritize leads, opportunities, and customers based on their likelihood to convert, churn, or buy. Forecasting can help predict future sales, revenue, or demand based on historical data and trends. Recommendations can help suggest the best products, services, or actions for each customer based on their preferences, behavior, and needs."
NEW QUESTION # 25
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