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Salesforce Exam Salesforce AI Associate Topic 1 Question 40 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 40
Topic #: 1
[All Salesforce AI Associate Questions]

What are the key components of the data quality standard?

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Suggested Answer: B

''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.''


by Irene at Mar 22, 2025, 04:20 AM

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Lynelle
22 hours ago
B) Accuracy, Completeness, Consistency - the data quality triple threat, baby! *flexes data muscles*
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Margurite
4 days ago
C) Reviewing, Updating, Archiving - gotta keep that data squeaky clean, am I right?
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Wynell
7 days ago
I think it's a combination of both sets of components, they all play a role in ensuring data quality.
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Sommer
8 days ago
I believe Naming, Formatting, Monitoring are also crucial for data quality.
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Cammy
11 days ago
B) Accuracy, Completeness, Consistency - the trifecta of data quality nirvana! *mic drop*
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Gearldine
14 days ago
I agree with Quiana, those are important for data quality.
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Milly
16 days ago
Wait, is there an option for 'All of the above'? Seems like they're all pretty important to me.
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Cecily
17 days ago
B) Accuracy, Completeness, Consistency - easy peasy, that's the data quality holy trinity!
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Quiana
18 days ago
I think the key components are Accuracy, Completeness, Consistency.
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France
20 days ago
A) Naming, formatting, Monitoring - those are the essential building blocks for data quality, no doubt about it.
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Gladys
8 days ago
A) Naming, formatting, Monitoring - those are the essential building blocks for data quality, no doubt about it.
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Rikki
24 days ago
Hmm, I'd go with C) Reviewing, Updating, Archiving. Gotta make sure your data stays fresh, you know?
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Maryann
25 days ago
B) Accuracy, Completeness, Consistency - that's the classic data quality framework right there!
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