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Salesforce Salesforce-AI-Associate Exam Sample Questions

Question # 1

Cloud Kicks wants to optimize its business operations by incorporating AI into CRM. What should the company do first to prepare its data for use with AI?

A. Remove biased data.

B. Determine data availability

C. Determine data outcomes.

Answer

B. Determine data availability

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Question # 2

What is an example of Salesforce's Trusted AI Principle of Inclusivity in practice?

A. Testing models with diverse datasets

B. Striving for model explain ability

C. Working with human rights experts

Answer

A. Testing models with diverse datasets

Explanation: “An example of Salesforce’s Trusted AI Principle of Inclusivity in practice is testing models with diverse datasets. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Testing models with diverse datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain.”

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Question # 3

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 # 4

What are the key components of the data quality standard?

A. Naming, formatting, Monitoring

B. Accuracy, Completeness, Consistency

C. Reviewing, Updating, Archiving

Answer

B. Accuracy, Completeness, Consistency

Explanation: “Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency.

Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.”

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Question # 5

The Cloud technical team is assessing the effectiveness of their AI development processes? Which established Salesforce Ethical Maturity Model should the team use to guide the development of trusted AI solution?

A. Ethical AI Prediction Maturity Model

B. Ethical AI Process Maturity Model

C. Ethical AI practice Maturity Model

Answer

B. Ethical AI Process Maturity Model

Explanation: “The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use to guide the development of trusted AI solutions. The Ethical AI Process Maturity Model is a framework that helps assess and improve the ethical and responsible practices and processes involved in developing and deploying AI systems. The Ethical AI Process Maturity Model consists of five levels of maturity: Ad Hoc, Aware, Defined, Managed, and Optimized. The Ethical AI Process Maturity Model can help guide the development of trusted AI solutions by providing a roadmap and best practices for achieving higher levels of ethical maturity.”

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