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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	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	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.

Salesforce Certified AI Associate Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
What is a potential source of bias in training data for AI models?

A. The data is collected from a diverse range of sources and demographics.
B. The data is skewed toward is particular demographic or source.
C. The data is collected in area time from sources systems.

Answer: B

Explanation:
"A potential source of bias in training data for AI models is that the datais skewed toward a particular demographic or source. Skewed data means that the data is not balanced or representative of the target population or domain. Skewed data can introduce or exacerbate bias in AI models, as they may overfit or underfit the modelto a specific subset of data. For example, skewed data can lead to bias if the data is collected from a limited or biased demographic or source, such as a certain age group, gender, race, location, or platform."

NEW QUESTION # 20
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?

A. Different types of AI that can be applied in Salesforce
B. Different types of automation tools used in Salesforce
C. Different types of data models used in Salesforce

Answer: A

Explanation:
Predictive analytics, machine learning, natural language processing (NLP), and computer vision are all types of artificial intelligence technologies that can be applied in Salesforce to enhance various aspects of business operations and customer interactions. Predictive analytics uses historical data to make predictions about future events. Machine learning involves algorithms that can learn from and make decisions based on data.
NLP is concerned with the interactions between computers and humans using natural language, and computer vision interprets and processes visual information from the world to make sense of it in the way humans do.
Salesforce harnesses these AI technologies, particularly through its Einstein platform, to provide powerful tools that help businesses automate tasks, make better decisions, and offer more personalized services. For more on how Salesforce utilizes these AI technologies, you can explore the Einstein AI services documentation at Salesforce Einstein.

NEW QUESTION # 21
Why is it critical to consider privacy concerns when dealing with AI and CRM data?

A. Ensures compliance with laws and regulations
B. Increases the volume of data collected
C. Confirms the data is accessible to all users

Answer: A

Explanation:
Explanation
"It is critical to consider privacy concerns when dealing with AI and CRM data because it ensures compliance with laws and regulations. Data privacy is the right of individuals to control how their personal data is collected, used, shared, or stored by others. Data privacy laws and regulations are legal frameworks that define and enforce the rights and obligations of data subjects, data controllers, and data processors regarding personal data. Data privacy laws and regulations vary by country, region, or industry, and may impose different requirements or restrictions on how AI and CRM data can be handled."

NEW QUESTION # 22
How does data quality impact the trustworthiness of Al-driven decisions?

A. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
B. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
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 # 23
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. Volume
B. Consistency
C. Performance

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 # 24
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