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Salesforce Salesforce-AI-Associate Exam Sample Questions

Question # 1

What is the main focus of the Accountability principle in Salesforce's Trusted AI Principles?

A. Safeguarding fundamental human rights and protecting sensitive data

B. Taking responsibility for one's actions toward customers, partners, and society

C. Ensuring transparency In Al-driven recommendations and predictions

Answer

B. Taking responsibility for one's actions toward customers, partners, and society

Explanation: “The main focus of the Accountability principle in Salesforce’s Trusted AI Principles is taking responsibility for one’s actions toward customers, partners, and society. Accountability means that AI systems should be designed and developed with respect for the impact and consequences of their actions on others. Accountability also means that AI developers and users should be aware of and adhere to the ethical, legal, and regulatory standards and expectations of their industry and domain.”

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Question # 2

A marketing manager wants to use AI to better engage their customers. Which functionality provides the best solution?

A. Journey Optimization

B. Bring Your Own Model

C. Einstein Engagement

Answer

C. Einstein Engagement

Explanation: “Einstein Engagement provides the best solution for a marketing manager who wants to use AI to better engage their customers. Einstein Engagement is a feature that uses AI to optimize email marketing campaigns by providing insights and recommendations on the best time, frequency, content, and subject lines to send emails to each customer. Einstein Engagement can help increase customer engagement, retention, and loyalty by delivering personalized and relevant messages.”

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Question # 3

How does poor data quality affect predictive and generative AI models?

A. Creates inaccurate results

B. Increases raw data volume

C. Decreases storage efficiency

Answer

A. Creates inaccurate results

Explanation: Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis. Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.

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Question # 4

What are the three commonly used examples of AI in CRM?

A. Predictive scoring, reporting, Image classification

B. Predictive scoring, forecasting, recommendations

C. Einstein Bots, face recognition, recommendations

Answer

B. Predictive scoring, forecasting, recommendations

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.”

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Question # 5

Cloud Kicks wants to use Einstein Prediction Builder to determine a customer’s likelihood of buying specific products; however, data quality is a _____________. How can data quality be assessed quality?

A. Build a Data Management Strategy.

B. Build reports to expire the data quality.

C. Leverage data quality apps from AppExchange

Answer

C. Leverage data quality apps from AppExchange

Explanation: Data quality apps from the AppExchange can help Cloud Kicks assess and improve their data quality, which is essential for accurate predictions using Einstein Prediction Builder. 

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