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IAPP AIGP Exam - 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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Brittney
4 months ago
Not sure if that's enough to fix all issues though.
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Jerry
4 months ago
Yup, data cleansing is the way to go!
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Gail
4 months ago
Wait, is model disgorgement even a real thing?
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Casie
4 months ago
I thought it was model inversion?
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Joanna
4 months ago
Definitely data cleansing!
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Lucy
5 months ago
I feel like data cleansing is definitely the answer, especially since we talked about it in class as a key step in preprocessing.
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Weldon
5 months ago
Model inversion sounds familiar, but I can't recall if it relates to cleaning data or something else entirely.
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Heike
5 months ago
I remember practicing a question about data de-duplication, but it seems more focused on removing duplicates rather than fixing improperly used data.
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Johnna
5 months ago
I think data cleansing is the right term for removing bad data, but I'm not entirely sure if that's the only technique we should consider.
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Willard
5 months ago
Data de-duplication? Model inversion? What kind of weird techniques are those? I'm just going to go with data cleansing - that's the standard way to handle bad data, right?
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Alba
5 months ago
Hmm, I'm not totally sure about this one. Is data cleansing the right approach, or is there something more specific I should be thinking about? I'll have to think it through carefully.
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Noah
5 months ago
This looks like a straightforward question about data cleaning techniques. I'm pretty confident I can handle this one.
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Kayleigh
5 months ago
Okay, data cleansing seems like the obvious answer here. But I want to double-check the other options just to be sure I'm not missing something. Gotta cover all my bases on this exam.
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Sueann
6 months ago
I think the key here is understanding how the personal experiences and perceptions of the people involved in the communication can influence how the message is interpreted. Option C seems to capture that idea the best, so I'm leaning towards selecting that one.
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Cathern
6 months ago
Hmm, I'm a bit confused. The question mentions that the marketing team wants to nurture those leads, but it doesn't explicitly say to add them to a Nurture Stream. I'm not sure if that's the right approach. Maybe I should re-read the question carefully.
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Kent
2 years ago
I believe it's data cleansing because it helps remove any errors or inconsistencies in the data.
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Emilio
2 years ago
I'm not sure, but I think it might be data de-duplication.
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Gladys
2 years 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
2 years ago
Exactly! Data de-duplication is all about removing those duplicate entries to keep the data clean.
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Destiny
2 years ago
C) Data de-duplication.
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Kris
2 years ago
A) Data cleansing.
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Britt
2 years ago
Model inversion? Nah, that's probably just a fancy term for making the model do the Macarena.
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Jamal
2 years ago
C) Data de-duplication.
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Ligia
2 years ago
A) Data cleansing.
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Blair
2 years ago
I agree with Yvonne, data cleansing is the way to go.
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Ilona
2 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
2 years ago
B) Model inversion.
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Blair
2 years ago
Absolutely, cleaning up the data is crucial for a well-functioning ML system.
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Brett
2 years ago
C) Data de-duplication.
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Brock
2 years ago
A) Data cleansing.
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Yvonne
2 years ago
I think the technique is data cleansing.
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