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Exam Salesforce-AI-Associate Collection, Salesforce-AI-Associate Training For Exam

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.

Topic 2

Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply the Trusted AI Principles of Salesforce to given scenarios.

Topic 3

Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.

Topic 4

AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.

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Salesforce Certified AI Associate Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
What is an example of ethical debt?

A. Launching an AI feature after discovering a harmful bias
B. Delaying an AI product launch to retrain an AI data model
C. Violating a data privacy law and falling to pay fines

Answer: A

Explanation:
Explanation
"Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is a term that describes the potential harm or risk caused by unethical or irresponsible decisions or actions related to AI systems. Ethical debt can accumulate over time and have negative consequences for users, customers, partners, or society. For example, launching an AI feature after discovering a harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect their trust, satisfaction, or well-being."

NEW QUESTION # 85
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

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

Answer: C

Explanation:
A benefit of data quality and transparency as it pertains to bias in generated AI is that the chances of bias are mitigated. High data quality ensures that AI models are trained on accurate and representative data, reducing the risk of biased outcomes. Transparency in AI processes helps stakeholders understand how decisions are made, allowing for the identification and correction of potential biases. Together, these practices contribute to the development of fairer and more accountable AI systems. Salesforce highlights the importance of these principles in its AI practices, particularly through its ethical AI framework, which advocates for fairness and accountability. More on Salesforce's commitment to promoting unbiased AI can be found in their AI ethics guidelines at Salesforce AI Ethics.

NEW QUESTION # 86
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, withmissing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company's operations?

A. The diversity of product recommendations Is Improved.
B. The accuracy of product recommendations is hindered.
C. The response time for product recommendations is stalled.

Answer: B

Explanation:
"The incomplete data quality will impact the company's operations by hindering the accuracy of product recommendations. Incomplete data means that the data is missing some values or attributes that are relevant for the AI task. Incomplete data can affect the performance and reliability of AI models, as they may not have enough information to learn from or make accurate predictions. For example, incomplete customer records can affect the quality of product recommendations, as the AI model may not be able to capture the customers' preferences, behavior, or needs."

NEW QUESTION # 87
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

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.

NEW QUESTION # 88
What role does data quality play in the ethical us of AI applications?

A. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
B. Low-quality data reduces the risk of unintended bias as the datais not overfitted to demographic groups.
C. High-quality data is essential for ensuringunbased and for fair AI decisions, promoting ethical use, and preventing discrimi...

Answer: C

Explanation:
"High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data."

NEW QUESTION # 89
......

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