Test Salesforce-AI-Associate Dumps - Salesforce-AI-Associate Exam Format

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
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.

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Salesforce Certified AI Associate Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
What is a key characteristic of machine learning in the context of AI capabilities?

A. Can perfectly mimic human intelligence and decision-making
B. Relies on preprogrammed rules to make decisions
C. Uses algorithms to learn from data and make decisions

Answer: C

Explanation:
Explanation
"Machine learning is a key characteristic of AI capabilities that uses algorithms to learn from data and make decisions. Machine learning is a branch of AI that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can analyze data, identify patterns, and make predictions or recommendations based on the data."

NEW QUESTION # 83
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce's Trusted AI Principle of Transparency?

A. Communicate how risk factors such as credit score can impact customer eligibility.
B. Incorporate customer feedback into the model's continuous training.
C. Flag sensitive variables and their proxies to prevent discriminatory lending practices.

Answer: C

Explanation:
Explanation
"Flagging sensitive variables and their proxies to prevent discriminatory lending practices is how they should implement Salesforce's Trusted AI Principle of Transparency. Transparency is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with. Flagging sensitive variables and their proxies means identifying and marking variables that can potentially cause discrimination or unfair treatment based on a person's identity or characteristics, such as age, gender, race, income, or credit score. Flagging sensitive variables and their proxies can help implement Transparency by allowing users to understand and evaluate the data used or generated by AI systems."

NEW QUESTION # 84
What are the key components of the data quality standard?

A. Naming, formatting, Monitoring
B. Accuracy, Completeness, Consistency
C. Reviewing, Updating, Archiving

Answer: B

Explanation:
Explanation
"Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources."

NEW QUESTION # 85
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?

A. Number of variables ipn the dataset
B. Age of the dataset
C. Size of the dataset

Answer: C

Explanation:
"The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect thefeasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data."

NEW QUESTION # 86
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company's recommendations?

A. The accuracy of the product
B. The diversity of the product
C. The response time for the product

Answer: A

Explanation:
Incomplete data quality negatively impacts the accuracy of product recommendations made by Cloud Kicks.
If data is missing or incomplete, the AI models used for product recommendation may not have enough information to accurately predict customer preferences and behavior. This leads to recommendations that may not align well with customer needs, reducing customer satisfaction and potentially affecting sales.
Ensuring complete and accurate data is crucial for effective recommendation systems. Salesforce discusses the impact of data quality on AI outcomes and strategies to enhance data integrity in their documentation on AI and data management, which can be referenced at Data Management for AI.

NEW QUESTION # 87
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