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Salesforce Certified AI Associate Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
How does poor data quality affect predictive and generative AI models?

A. Creates inaccurate results
B. Decreases storage efficiency
C. Increases raw data volume

Answer: A

Explanation:
Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis.
Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.

NEW QUESTION # 14
How is natural language processing (NLP) used in the context of AI capabilities?

A. To interpret and understand programming language
B. To understand and generate human language
C. To cleanse and prepare data for AI implementations

Answer: B

Explanation:
Explanation
"Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts."

NEW QUESTION # 15
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 diversity of the product
B. The accuracy of the product
C. The response time for the product

Answer: B

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 # 16
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 # 17
Which type of bias imposes a system 's values on others?

A. Association
B. Societal
C. Automation

Answer: B

NEW QUESTION # 18
......

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Professional Salesforce - Salesforce-AI-Associate - Latest Salesforce Certified AI Associate Exam Exam Simulator

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Salesforce Certified AI Associate Exam Sample Questions (Q13-Q18):

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