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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?

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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
4 months ago
I believe it's data cleansing because it helps remove any errors or inconsistencies in the data.
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Emilio
4 months ago
I'm not sure, but I think it might be data de-duplication.
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Gladys
4 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
4 months ago
Exactly! Data de-duplication is all about removing those duplicate entries to keep the data clean.
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Destiny
4 months ago
C) Data de-duplication.
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Kris
4 months ago
A) Data cleansing.
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Britt
5 months ago
Model inversion? Nah, that's probably just a fancy term for making the model do the Macarena.
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Jamal
4 months ago
C) Data de-duplication.
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Ligia
4 months ago
A) Data cleansing.
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Blair
5 months ago
I agree with Yvonne, data cleansing is the way to go.
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Ilona
5 months 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
4 months ago
B) Model inversion.
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Blair
4 months ago
Absolutely, cleaning up the data is crucial for a well-functioning ML system.
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Brett
4 months ago
C) Data de-duplication.
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Brock
4 months ago
A) Data cleansing.
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Yvonne
5 months ago
I think the technique is data cleansing.
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