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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

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

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

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Salesforce Certified AI Associate Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
What is a possible outcome of poor data quality?

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

Answer: C

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 Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task.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 # 80
What does the term "data completeness" refer to in the context of data quality?

A. The process of aggregating multiple datasets from various databases
B. The ability to access data from multiple sources in real time
C. The degree to which all required data points are present in the dataset

Answer: C

Explanation:
Data completeness is a measure of data quality that assesses whether all required data points are present in a dataset. It checks for missing values or gaps in data necessary for accurate analysis and decision-making. In the context of Salesforce, ensuring data completeness is crucial for the effectiveness of CRM operations, reporting, and AI-driven applications like Salesforce Einstein, which rely on complete data to function optimally. Salesforce provides various tools and features, such as data validation rules and batch data import processes, that help maintain data completeness across its platform. Detailed guidance on managing data quality in Salesforce can be found in the Salesforce Help documentation on data management at Salesforce Help Data Management.

NEW QUESTION # 81
In the context of Salesforce's Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?

A. Empower users to off all skill level to build AI application with clicks, not code.
B. Empower users to solve challenging technical problems using neural networks.
C. Empower users to contribute to the growing body of knowledge of leading AIresearch.

Answer: A

Explanation:
"The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment isone of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery."

NEW QUESTION # 82
A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?

A. Completeness
B. Duplication
C. Usage

Answer: A

Explanation:
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness.
Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights.
Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.

NEW QUESTION # 83
What is the best method to safeguard customer data privacy?

A. Archive customer data on a recurring schedule.
B. Automatically anonymize all customer data.
C. Track customer data consent preferences.

Answer: C

Explanation:
"Tracking customer data consent preferences is the best method to safeguard customer data privacy. Data privacy is the right of individuals to control how their personal data is collected, used, shared, or stored by others. Tracking customer data consent preferences means respecting and honoring the choices and preferencesof customers regarding their personal data. Tracking customer data consent preferences can help ensure compliance with data privacy laws and regulations, as well as build trust and loyalty with customers."

NEW QUESTION # 84
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