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Exam Salesforce-AI-Associate Syllabus, Salesforce-AI-Associate Practice Braindumps

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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 (Q15-Q20):

NEW QUESTION # 15
What is an example of ethical debt?

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

Answer: B

Explanation:
"Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is a term that describes the potential harm or risk caused by unethical or irresponsible decisions or actions related to AIsystems. Ethical debt can accumulate over time and have negative consequences for users, customers, partners, or society. For example, launching an AI feature after discovering a harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect their trust, satisfaction, or well-being."

NEW QUESTION # 16
What is the rile of data quality in achieving AI business Objectives?

A. Data quality is required to create accurate AI data insights.
B. Data quality is unnecessary because AI can work with all data types.
C. Data quality is important for maintain Ai data storage limits

Answer: A

Explanation:
"Data quality is required to create accurate AI data insights. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Data quality can also affect the accuracy and validity of AI data insights, as they reflect the quality of the data used or generated by AI systems."

NEW QUESTION # 17
How does a data quality assessment impact business outcome for companies using AI?

A. Accelerates the delivery of new AI solutions
B. Provides a benchmark for AI predictions
C. Improves the speed of AI recommendations

Answer: B

Explanation:
Explanation
"A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AI predictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain."

NEW QUESTION # 18
What is the most likely impact that high-quality data will have on customer relationships?

A. Higher customer acquisition costs
B. Improved customer trust and satisfaction
C. Increased brand loyalty

Answer: B

Explanation:
Explanation
"The most likely impact that high-quality data will have on customer relationships is improved customer trust and satisfaction. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can improve customer relationships by enabling AI systems to provide personalized and relevant products, services, or solutions that meet the customers' expectations, needs, and interests. High-quality data can also improve customer trust and satisfaction by reducing errors, delays, or waste in customer interactions."

NEW QUESTION # 19
How does a data quality assessment impact business outcome for companies using AI?

A. Accelerates the delivery of new AI solutions
B. Provides a benchmark for AI predictions
C. Improves the speed of AI recommendations

Answer: B

Explanation:
"A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AIpredictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain."

NEW QUESTION # 20
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