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Salesforce-AI-Associate Exam Quick Prep - Pdf Salesforce-AI-Associate Braindumps

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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

Topic 3

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 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 (Q25-Q30):

NEW QUESTION # 25
How does an organization benefit from using AI to personalize the shopping experience of online customers?

A. Customers are more likely to be satisfied with their shopping experience.
B. Customers are morelikely to share personal information with a site that personalizes their experience.
C. Customers are more likely to visit competitor sites that personalize their experience.

Answer: A

Explanation:
"An organization benefits from using AI to personalize the shopping experience of online customers by increasing customer satisfaction. AI can help provide customized and relevant product recommendations, offers, or content based on the customers' preferences, behavior, or needs. AI can also help create a more engaging and interactive shopping experience by using natural language processing (NLP) or computer vision techniques. Personalized shopping experiences can improve customer satisfaction by meeting their expectations, needs, and interests."

NEW QUESTION # 26
What is an example of ethical debt?

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

Answer: B

Explanation:
Ethical debt refers to the long-term negative consequences of prioritizing speed or convenience over responsible AI development practices. Ethical debt accumulates when AI systems are deployed despite known ethical concerns, such as bias, privacy violations, or transparency issues.
Option A (Correct): Launching an AI feature after discovering harmful bias is a clear example of ethical debt because it disregards the ethical obligation to ensure fairness and non-discrimination in AI outcomes. Ignoring bias can lead to systemic issues that are difficult and costly to correct later.
Option B (Incorrect): Violating a data privacy law and failing to pay fines is a legal issue rather than an example of ethical debt. While related, ethical debt pertains more to AI decision-making and development choices.
Option C (Incorrect): Delaying an AI product launch to retrain an AI model is a responsible action that helps avoid ethical debt, rather than an example of it. This demonstrates an effort to mitigate bias and improve AI fairness before deployment.

NEW QUESTION # 27
What is a possible outcome of poor data quality?

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

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 # 28
How is natural language processing (NLP) used in the context of AI capabilities?

A. To interpret and understand programminglanguage
B. To cleanse and prepare data for AI implementations
C. To understand and generate human language

Answer: C

Explanation:
"Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts."

NEW QUESTION # 29
Which type of AI can enhance customer service agents' email responses by analyzing the written content of previous emails?

A. Deep learning
B. Machine learning
C. Natural language processing

Answer: C

Explanation:
Natural language processing (NLP) is the type of AI that can enhance customer service agents' email responses by analyzing the written content of previous emails. NLP technologies interpret and generate human language, allowing AI systems to understand, respond to, and even anticipate customer needs based on email interactions. This capability helps in crafting more relevant, accurate, and personalized email responses, improving customer service quality. Salesforce utilizes NLP in its Einstein AI platform to augment various customer service functions. More about Salesforce Einstein's NLP capabilities can be found on the Salesforce Einstein page at Salesforce Einstein NLP.

NEW QUESTION # 30
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