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?

ANaming, formatting, Monitoring

BAccuracy, Completeness, Consistency

CReviewing, Updating, Archiving

Show Suggested Answer

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

2 months ago

B) Accuracy, Completeness, Consistency - the data quality triple threat, baby! *flexes data muscles*

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Ailene

1 months ago

B) Accuracy, Completeness, Consistency - the holy trinity of data quality, gotta keep it clean!

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Kimbery

1 months ago

A) Naming, formatting, Monitoring - those are important too, can't have messy data!

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Margurite

2 months ago

C) Reviewing, Updating, Archiving - gotta keep that data squeaky clean, am I right?

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Wynell

2 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

2 months ago

I believe Naming, Formatting, Monitoring are also crucial for data quality.

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Cammy

2 months ago

B) Accuracy, Completeness, Consistency - the trifecta of data quality nirvana! *mic drop*

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Yuki

19 days ago

C) Reviewing, Updating, Archiving

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Sueann

20 days ago

B) Accuracy, Completeness, Consistency - the trifecta of data quality nirvana! *mic drop*

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Pa

24 days ago

A) Naming, formatting, Monitoring

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Fatima

27 days ago

A) I agree, monitoring is key to ensuring data quality over time.

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Jacinta

28 days ago

C) Reviewing, Updating, Archiving - all crucial for maintaining data quality standards.

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Erasmo

1 months ago

B) Accuracy, Completeness, Consistency - definitely the trifecta for data quality.

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Terrilyn

2 months ago

A) Naming, formatting, Monitoring - those are important too!

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Gearldine

2 months ago

I agree with Quiana, those are important for data quality.

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Milly

3 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

3 months ago

B) Accuracy, Completeness, Consistency - easy peasy, that's the data quality holy trinity!

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Quiana

3 months ago

I think the key components are Accuracy, Completeness, Consistency.

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France

3 months ago

A) Naming, formatting, Monitoring - those are the essential building blocks for data quality, no doubt about it.

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Catalina

2 months ago

C) Reviewing, Updating, Archiving - these steps are important for maintaining data quality standards over time.

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Goldie

2 months ago

A) Reviewing, Updating, Archiving are important for maintaining data quality standards.

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Reid

2 months ago

B) Accuracy, Completeness, Consistency are also key components to consider.

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Carey

2 months ago

A) Naming, formatting, Monitoring are crucial for ensuring data quality.

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Edison

2 months ago

B) Accuracy, Completeness, Consistency - those are also crucial components to ensure high data quality.

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Gladys

2 months ago

A) Naming, formatting, Monitoring - those are the essential building blocks for data quality, no doubt about it.

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Rikki

3 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

3 months ago

B) Accuracy, Completeness, Consistency - that's the classic data quality framework right there!

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