SALESFORCE-AI-ASSOCIATE DISCOUNT, RELIABLE SALESFORCE-AI-ASSOCIATE TEST OBJECTIVES

Salesforce-AI-Associate Discount, Reliable Salesforce-AI-Associate Test Objectives

Salesforce-AI-Associate Discount, Reliable Salesforce-AI-Associate Test Objectives

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Salesforce Certified AI Associate Exam Sample Questions (Q98-Q103):

NEW QUESTION # 98
Which type of bias imposes a system 's values on others?

  • A. Automation
  • B. Association
  • C. Societal

Answer: C

Explanation:
"Societal bias is the type of bias that imposes a system's values on others. Societal bias isa type of bias that reflects the assumptions, norms, or values of a specific society or culture. Societal bias can affect the fairness and ethics of AI systems, as they may affect how different groups or domains are perceived, treated, or represented by AI systems. For example, societal bias can occur when AI systems impose a system's values on others, such as using Western standards of beauty or success to judge or rank people from other cultures."


NEW QUESTION # 99
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

Explanation:
Explanation
"Leveraging data quality apps from AppExchange is how data quality can be assessed. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Leveraging data quality apps from AppExchange means using third-party applications or solutions that can help measure, monitor, or improve data quality in Salesforce."


NEW QUESTION # 100
What is a key benefit of effective interaction between humans and AI systems?

  • A. Reduces the need for human involvement
  • B. Alerts humans to the presence of biased data
  • C. Leads to more informed and balanced decision making

Answer: C

Explanation:
Explanation
"A key benefit of effective interaction between humans and AI systems is that it leads to more informed and balanced decision making. Effective interaction means that humans and AI systems can communicate and collaborate with each other in a clear, natural, and respectful way. Effective interaction can help leverage the strengths and complement the weaknesses of both humans and AI systems. Effective interaction can also help increase trust, confidence, and satisfaction in using AI systems."


NEW QUESTION # 101
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

  • A. Chances of bIas and mitigated
  • B. Chances of bias are aggravated
  • C. Chances of bias are remove

Answer: A

Explanation:
Explanation
"Data quality and transparency can help mitigate the chances of bias in generative AI. Data quality means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can help mitigate bias by ensuring that the generative AI model learns from a balanced and representative sample of the target population or domain. Data transparency means that the data sources, methods, and processes are clear and open to inspection and verification. Data transparency can help mitigate bias by allowing users to understand and evaluate the data used or generated by the generative AI model."


NEW QUESTION # 102
What is a possible outcome of poor data quality?

  • A. Biases in data can be inadvertently learned and amplified by AI systems.
  • B. AI models maintain accuracy but have slower response times.
  • C. AI predictions become more focused and less robust.

Answer: A

Explanation:
"A possible outcome of poor data quality is that biases in data can be inadvertently learned and amplified by AI systems. 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 systems, as they may not have enough or correct information to learn from or make accurate predictions. Poor data quality can also introduce or exacerbate biases in data, such as human bias, societal bias, or confirmation bias, which can affect the fairness and ethics of AI systems."


NEW QUESTION # 103
......

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