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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 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 3	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 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 (Q85-Q90):

NEW QUESTION # 85
Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails.
Which data quality dimension should be assessed to reduce these communication Inefficiencies?

A. Duplication
B. Consent
C. Usage

Answer: A

Explanation:
"Duplication is the data quality dimension that should be assessed to reduce communication inefficiencies.
Duplication means that the data contains multiple copies or instances of the same record or value. Duplication can cause confusion, errors,or waste in data analysis and processing. For example, duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose."

NEW QUESTION # 86
What can bias in AI algorithms in CRM lead to?

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

Answer: C

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 # 87
A developer has a large amount of data, but it is scattered across different systems and is not standardized.
Which key data quality element should they focus on to ensure the effectiveness of the AI models?

A. Performance
B. Consistency
C. Volume

Answer: B

Explanation:
When data is scattered and not standardized, the key data quality element a developer should focus on is consistency. Consistency refers to the uniformity and standardization of data across different systems, which is crucial for integrating and analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce provides tools and practices for ensuring data consistency, such as data integration and management solutions that help standardize and synchronize data across platforms. For more information on Salesforce data management, refer to the Salesforce data management tools at Salesforce Data Management.

NEW QUESTION # 88
To avoid introducing unintended bias to an AI model, which type of data should be omitted?

A. Transactional
B. Engagement
C. Demographic

Answer: C

Explanation:
Explanation
"Demographic data should be omitted to avoid introducing unintended bias to an AI model. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairness and ethics of AI systems."

NEW QUESTION # 89
How does data quality impact the trustworthiness of Al-driven decisions?

A. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
B. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
C. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.

Answer: C

Explanation:
Explanation
"High-quality data improves the reliability and credibility of AI-driven decisions, fostering trust among users.
High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task.
High-quality data can improve the performance and reliability of AI systems, as they have enough and correct information to learn from and make accurate predictions. High-quality data can also improve the trustworthiness of AI-driven decisions, as users can have more confidence and satisfaction in using AI systems."

NEW QUESTION # 90
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