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Salesforce-AI-Associate Preparation Store - Salesforce-AI-Associate Test Braindumps

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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

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

Salesforce Certified AI Associate Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
What is a key characteristic of machine learning in the context of AI capabilities?

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

Answer: C

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 # 74
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 # 75
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 # 76
A healthcare company implements an algorithm to analyze patient data and assist in medical diagnosis.
Which primary role does data Quality play In this AI application?

A. Ensured compatibility of AI algorithms with the system's Infrastructure
B. Enhanced accuracy and reliability of medical predictions and diagnoses
C. Reduced need for healthcare expertise in interpreting AI outouts

Answer: B

Explanation:
Explanation
"Data quality plays a crucial role in enhancing the accuracy and reliability of medical predictions and diagnoses. Poor data quality can lead to inaccurate or misleading results, which can have serious consequences for patients' health and well-being. Therefore, it is important to ensure that the data used for AI applications in healthcare is accurate, complete, consistent, and relevant."

NEW QUESTION # 77
Which type of bias imposes a system 's values on others?

A. Societal
B. Association
C. Automation

Answer: A

NEW QUESTION # 78
......

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