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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
11 months ago
I believe it's data cleansing because it helps remove any errors or inconsistencies in the data.
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Emilio
11 months ago
I'm not sure, but I think it might be data de-duplication.
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Gladys
11 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
10 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
11 months 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
12 months ago
I agree with Yvonne, data cleansing is the way to go.
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Ilona
12 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
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
11 months ago
A) Data cleansing.
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Yvonne
12 months ago
I think the technique is data cleansing.
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