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2025 Salesforce-AI-Associate: Salesforce Certified AI Associate Exam Useful Vce Test Simulator

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 (Q52-Q57):

NEW QUESTION # 52
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>

A. Inclusivity
B. Accountability
C. Transparency

Answer: A

Explanation:
Explanation
"Conducting a series of Consequence Scanning workshops to support testing diverse datasets is an action that practices Salesforce's Trusted AI Principle of Inclusivity. Inclusivity is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Conducting Consequence Scanning workshops means engaging with various stakeholders to identify and assess the potential impacts and implications of AI systems on different groups or domains. Conducting Consequence Scanning workshops can help practice Inclusivity by ensuring that diverse datasets are used to test and evaluate AI systems."

NEW QUESTION # 53
How does data quality impact the trustworthiness of Al-driven decisions?

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

Answer: A

NEW QUESTION # 54
Which best describes the different between predictive AI and generative AI?

A. Predictive AI uses machine learning to classes or predict output from its input data whereas generative AI does not use machine learning to generate its output
B. Predictive new and original output for a given input.
C. Predictive AI and generative have the same capabilities differ in the type of input theyreceive: predictive AI receives raw data whereas generation AI receives natural language.

Answer: B

Explanation:
"The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques to generate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos."

NEW QUESTION # 55
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

A. Chances of bias are aggravated
B. Chances of bIas and mitigated
C. Chances of bias are remove

Answer: B

Explanation:
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 data used or generated by the generative AI model."

NEW QUESTION # 56
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

A. Chances of bias are aggravated
B. Chances of bIas and mitigated
C. Chances of bias are remove

Answer: B

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 # 57
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Ron Black Ron Black

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