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IAPP Exam AIGP Topic 7 Question 2 Discussion

Actual exam question for IAPP's AIGP exam

Question #: 2
Topic #: 7

[All AIGP Questions]

What is the technique to remove the effects of improperly used data from an ML system?

AData cleansing.

BModel inversion.

CData de-duplication.

DModel disgorgement.

Show Suggested Answer

Suggested Answer: D

Model disgorgement is the technique used to remove the effects of improperly used data from an ML system. This process involves retraining or adjusting the model to eliminate any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's outputs are not influenced by data that was not meant to be used or was used incorrectly. Reference: AIGP Body of Knowledge on Data Management and Model Integrity.

by Alayna at Jun 24, 2024, 05:01 PM

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Kent

12 months ago

I believe it's data cleansing because it helps remove any errors or inconsistencies in the data.

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Emilio

12 months ago

I'm not sure, but I think it might be data de-duplication.

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Gladys

12 months ago

Data de-duplication, huh? Sounds like a fancy way to say 'delete all those pesky duplicates and keep it clean!'

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Noelia

11 months ago

Exactly! Data de-duplication is all about removing those duplicate entries to keep the data clean.

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Destiny

11 months ago

C) Data de-duplication.

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Kris

11 months ago

A) Data cleansing.

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Britt

1 years ago

Model inversion? Nah, that's probably just a fancy term for making the model do the Macarena.

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Jamal

11 months ago

C) Data de-duplication.

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Ligia

11 months ago

A) Data cleansing.

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Blair

1 years ago

I agree with Yvonne, data cleansing is the way to go.

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Ilona

1 years ago

Data cleansing sounds like the way to go. Gotta clean up that messy data before it messes up the system!

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Paris

11 months ago

B) Model inversion.

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Blair

11 months ago

Absolutely, cleaning up the data is crucial for a well-functioning ML system.

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Brett

11 months ago

C) Data de-duplication.

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Brock

12 months ago

A) Data cleansing.

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Yvonne

1 years ago

I think the technique is data cleansing.

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