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Salesforce AI Associate Exam - 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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Veronica
2 months ago
Not sure if those are the only components we need to consider.
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Detra
3 months ago
Totally agree with B, it's essential for data quality.
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Tijuana
3 months ago
I thought Monitoring was key too.
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Micheal
3 months ago
Surprised that Archiving isn't mentioned more!
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Graham
3 months ago
Definitely Accuracy, Completeness, Consistency!
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Corinne
3 months ago
I feel like accuracy, completeness, and consistency are definitely the main components, but I can't recall if there's more to it.
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Elfrieda
4 months ago
I’m a bit confused; I thought naming and formatting were part of data management, not necessarily quality standards.
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Bobbie
4 months ago
I practiced a question similar to this, and I believe consistency was one of the key components mentioned.
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Linwood
4 months ago
I think I remember something about accuracy and completeness being really important for data quality, but I'm not entirely sure.
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Eleonora
4 months ago
Ah yes, I remember now - the key components are accuracy, completeness, and consistency. That covers the main criteria for ensuring high-quality data. I feel pretty confident about that.
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Jeanice
4 months ago
Okay, let me see. I believe the key components are naming, formatting, and monitoring. That sounds right, but I'll double-check my notes just to be sure.
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Leslee
5 months ago
Hmm, I'm not totally sure about this one. I know data quality is important, but I can't quite remember all the specific components. I'll have to think this through carefully.
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Gail
5 months ago
I think the key components of the data quality standard are accuracy, completeness, and consistency. That seems to cover the main aspects of ensuring data quality.
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Lynelle
10 months ago
B) Accuracy, Completeness, Consistency - the data quality triple threat, baby! *flexes data muscles*
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Ailene
10 months ago
B) Accuracy, Completeness, Consistency - the holy trinity of data quality, gotta keep it clean!
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Kimbery
10 months ago
A) Naming, formatting, Monitoring - those are important too, can't have messy data!
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Margurite
11 months ago
C) Reviewing, Updating, Archiving - gotta keep that data squeaky clean, am I right?
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Wynell
11 months 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
11 months ago
I believe Naming, Formatting, Monitoring are also crucial for data quality.
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Cammy
11 months ago
B) Accuracy, Completeness, Consistency - the trifecta of data quality nirvana! *mic drop*
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Yuki
9 months ago
C) Reviewing, Updating, Archiving
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Sueann
9 months ago
B) Accuracy, Completeness, Consistency - the trifecta of data quality nirvana! *mic drop*
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Pa
9 months ago
A) Naming, formatting, Monitoring
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Fatima
9 months ago
A) I agree, monitoring is key to ensuring data quality over time.
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Jacinta
9 months ago
C) Reviewing, Updating, Archiving - all crucial for maintaining data quality standards.
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Erasmo
10 months ago
B) Accuracy, Completeness, Consistency - definitely the trifecta for data quality.
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Terrilyn
10 months ago
A) Naming, formatting, Monitoring - those are important too!
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Gearldine
11 months ago
I agree with Quiana, those are important for data quality.
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Milly
11 months 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
11 months ago
B) Accuracy, Completeness, Consistency - easy peasy, that's the data quality holy trinity!
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Quiana
11 months ago
I think the key components are Accuracy, Completeness, Consistency.
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France
11 months ago
A) Naming, formatting, Monitoring - those are the essential building blocks for data quality, no doubt about it.
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Catalina
10 months ago
C) Reviewing, Updating, Archiving - these steps are important for maintaining data quality standards over time.
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Goldie
10 months ago
A) Reviewing, Updating, Archiving are important for maintaining data quality standards.
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Reid
10 months ago
B) Accuracy, Completeness, Consistency are also key components to consider.
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Carey
10 months ago
A) Naming, formatting, Monitoring are crucial for ensuring data quality.
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Edison
10 months ago
B) Accuracy, Completeness, Consistency - those are also crucial components to ensure high data quality.
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Gladys
11 months ago
A) Naming, formatting, Monitoring - those are the essential building blocks for data quality, no doubt about it.
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Rikki
11 months ago
Hmm, I'd go with C) Reviewing, Updating, Archiving. Gotta make sure your data stays fresh, you know?
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Maryann
11 months ago
B) Accuracy, Completeness, Consistency - that's the classic data quality framework right there!
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