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Salesforce Salesforce-AI-Associate Exam Sample Questions

Question # 1

What is one technique to mitigate bias and ensure fairness in AI applications?

A. Ongoing auditing and monitoring of data that is used in AI applications

B. Excluding data features from the Al application to benefit a population

C. Using data that contains more examples of minority groups than majority groups

Answer

A. Ongoing auditing and monitoring of data that is used in AI applications

Explanation:
A technique to mitigate bias and ensure fairness in AI applications is ongoing auditing and monitoring of the data used in AI applications. Regular audits help identify and address any biases that may exist in the data, ensuring that AI models function fairly and without prejudice. Monitoring involves continuously checking the performance of AI systems to safeguard against discriminatory outcomes. Salesforce emphasizes the importance of ethical AI practices, including transparency and fairness, which can be further explored through Salesforce’s AI ethics guidelines at Salesforce AI Ethics.

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Question # 2

Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results? What to a potential mason for this?

A. Poor data quality

B. The wrong product

C. Too much data

Answer

A. Poor data quality

Explanation: “Poor data quality is a potential reason for not seeing accurate results from an AI model. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor data quality can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions.”

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Question # 3

Which best describes the difference between predictive AI and generative Al?

A. Predictive AT uses machine learning to classify or predict outputs from its input data whereas generative Al does not use machine learning to generate its output.

B. Predictive Al uses machine learning to classify or predict outputs from its input data whereas generative Al uses machine learning to generate new and original output for 4 given input

C. Predictive Al and generative Al have the same capabilities but differ in the type of input they receive; predictive AT receives raw data whereas generative AT receives natural language.

Answer

B. Predictive Al uses machine learning to classify or predict outputs from its input data whereas generative Al uses machine learning to generate new and original output for 4 given input

Explanation: Predictive AI and generative AI represent two different applications of machine learning technologies. Predictive AI focuses on making predictions based on historical data. It analyzes past data to forecast future outcomes, such as customer churn or sales trends. On the other hand, generative AI is designed to generate new and original outputs based on the learned data patterns. This includes tasks like creating new images, text, or music that resemble the training data but do not duplicate it. Both types of AI use machine learning, but their objectives and outputs are distinct. For detailed differences and applications in a Salesforce context, Salesforce's guide on AI technologies is a helpful resource, accessible at Salesforce AI Technologies.

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Question # 4

What is a sensitive variable that car esc to bias?

A. Education level

B. Country

C. Gender

Answer

C. Gender

Explanation:

Gender is a sensitive variable that can lead to bias. A sensitive variable is a variable that can potentially cause discrimination or unfair treatment based on a person’s identity or characteristics. For example, gender is a sensitive variable because it can affect how people are perceived, treated, or represented by AI systems.

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Question # 5

Cloud Kicks wants to improve the quality of its AI model's predictions with the use of a large amount of data. Which data quality element should the company focus on?

A. Accuracy

B. Location

C. Volume

Answer

A. Accuracy

Explanation: To improve the quality of AI model predictions, Cloud Kicks should focus on the accuracy of the data. Accurate data ensures that the insights and predictions generated by AI models are reliable and valid. Data accuracy involves correcting errors, filling missing values, and verifying data sources to enhance the quality of information fed into the AI systems. Focusing on data accuracy helps in minimizing prediction errors and enhances the decision-making process based on AI insights. For more details on the importance of data quality in AI models, Salesforce provides extensive guidance in their documentation, which can be found at Data Quality and AI.

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