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Salesforce Exam Salesforce AI Associate Topic 4 Question 35 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 35
Topic #: 4
[All Salesforce AI Associate Questions]

Cloud Kicks wants to develop a solution to predict customers product interests based on historical dat

a. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.

Which data quality dimension is affected in this scenario?

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Suggested Answer: A

A technique to mitigate bias and ensure fairness in AI applications is ongoing auditing and monitoring of the data used in AI applications. Regular audits help identify and address any biases that may exist in the data, ensuring that AI models function fairly and without prejudice. Monitoring involves continuously checking the performance of AI systems to safeguard against discriminatory outcomes. Salesforce emphasizes the importance of ethical AI practices, including transparency and fairness, which can be further explored through Salesforce's AI ethics guidelines at Salesforce AI Ethics.


by Rosendo at Dec 11, 2024, 06:43 AM

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Jeannine
2 months ago
Wait, did someone really use a 'plckllst' instead of a proper dropdown? That's a whole new level of data shenanigans right there. Talk about consistency challenges!
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Audry
14 days ago
C: Yeah, it's crucial for accurate predictions to have consistent data across all regions.
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Dulce
17 days ago
B: I agree, it can really mess up the analysis if the data is not consistent.
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Melynda
24 days ago
A: Definitely a consistency issue. It's important to have uniform data entry methods.
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Jarvis
2 months ago
C) Consistency is the correct answer here. Inconsistent data capture methods across regions are bound to introduce issues with the integrity and reliability of the data.
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Rashida
4 days ago
A: It's important for all employees to use the same method to ensure accurate predictions.
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Cherry
5 days ago
B: Yeah, inconsistent data capture methods can definitely cause problems.
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Chi
9 days ago
A: I think the data quality dimension affected here is Consistency.
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Annamaria
2 months ago
Consistency is key when it comes to data quality. If the employees can't even agree on how to capture product categories, how can the company trust the insights they're getting?
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Darrin
2 months ago
Oof, the plckllst? Looks like someone had a bit too much fun with the keyboard. But seriously, this is a textbook case of consistency issues. Gotta get that data aligned, folks!
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Nenita
1 months ago
Agreed, consistency is key for accurate predictions.
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Rosenda
2 months ago
Yeah, definitely a consistency issue. Need to standardize that data input.
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Jacinta
2 months ago
But could it also be accuracy, since the text field may not accurately capture the product category?
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Lynelle
2 months ago
The lack of consistency in data capture definitely affects the overall data quality. A standardized approach across all locations is crucial for reliable analysis.
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Eden
1 months ago
A: I believe it's consistency, since the data is not uniform across all locations.
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Carolann
1 months ago
C: So, which data quality dimension do you think is affected in this scenario?
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Bethanie
1 months ago
B: Definitely, having different methods in different regions can lead to inaccurate predictions.
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Pearly
2 months ago
A: I think the lack of consistency in data capture is a big issue.
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Corinne
2 months ago
I agree with Glory, because the data is not consistent across regions.
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Glory
3 months ago
I think the data quality dimension affected is consistency.
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