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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

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

Topic 2

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

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

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.

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Salesforce Certified AI Associate Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
What can bias in AI algorithms in CRM lead to?

A. Personalization and target marketing changes
B. Ethical challengesin CRM systems
C. Advertising cost increases

Answer: B

Explanation:
"Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness andethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers' identity or characteristics."

NEW QUESTION # 65
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?

A. Number of variables ipn the dataset
B. Size of the dataset
C. Age of the dataset

Answer: B

Explanation:
"The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect thefeasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data."

NEW QUESTION # 66
What is a key challenge of human AI collaboration in decision-making?

A. Creates a reliance on AI, potentially leading to less critical thinking and oversight
B. Reduce the need for human involvement in decision-making processes
C. Leads to move informed and balanced decision-making

Answer: A

Explanation:
"A key challenge of human-AI collaboration in decision-making is that it creates a reliance on AI, potentially leading to less critical thinking and oversight. Human-AI collaboration is a process that involves humans and AI systems working together to achieve a common goal or task. Human-AI collaboration can have many benefits, such as leveraging the strengths and complementing the weaknesses of both humans and AI systems. However, human-AI collaboration can also pose some challenges, such as creating a reliance on AI, potentially leading to less critical thinking and oversight. For example, human-AI collaboration can create a reliance on AI if humans blindly trust or follow the AI recommendations without questioning or verifying their validity or rationale."

NEW QUESTION # 67
How is natural language processing (NLP) used in the context of AI capabilities?

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

Answer: B

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 # 68
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:
"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 dataused or generated by the generative AI model."

NEW QUESTION # 69
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