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IAPP Exam AIGP Topic 2 Question 16 Discussion

Actual exam question for IAPP's AIGP exam
Question #: 16
Topic #: 2
[All AIGP Questions]

To maintain fairness in a deployed system, it is most important to?

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Suggested Answer: B

To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.


by Sherrell at Sep 15, 2024, 08:30 PM

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Hassie
16 days ago
Option B for sure. Gotta keep that data fresh, am I right? Can't have the model getting stale and biased on us.
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Stevie
17 days ago
Ha! Fairness? In a deployed system? Good one. I'll go with option A to protect against personal data loss. That's the least of our worries, am I right?
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Brice
18 days ago
Hmm, I'm not sure. Option D about optimizing computational resources and data sounds like a good way to keep the system efficient and scalable.
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Barney
22 days ago
Option C seems like the best choice to me. Detecting anomalies that require new training data is key to preventing biases and maintaining fairness.
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Noel
4 days ago
I think monitoring for data drift is also important to maintain accuracy.
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Sueann
6 days ago
I agree, detecting anomalies is crucial for fairness in the system.
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Kirk
24 days ago
I think option B is the most important for maintaining fairness. Monitoring for data drift is crucial to ensure the model's performance and accuracy stay consistent over time.
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Sherell
26 days ago
B sounds like the way to go. Can't have the model going off the rails due to pesky data drift!
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Vicki
7 days ago
Absolutely, we need to stay on top of any anomalies to keep the system running smoothly.
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Kenny
10 days ago
C) Detect anomalies outside established metrics that require new training data.
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Matthew
15 days ago
B) Monitor for data drift that may affect performance and accuracy.
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Jamey
1 months ago
I believe optimizing computational resources is key for fairness.
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Sage
1 months ago
I agree with Tiara, data drift can really impact performance.
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Jesse
1 months ago
Haha, D for sure! Gotta optimize those resources and data to make sure the system runs like a well-oiled machine.
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Daniel
1 months ago
I'm going with C. Detecting anomalies outside established metrics will help identify when new training data is needed to keep the model up-to-date.
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Nieves
6 days ago
C is definitely important for keeping the model updated with new training data.
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Sherly
9 days ago
I agree, but D is crucial too. Optimizing resources ensures efficiency in the system.
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Rochell
14 days ago
True, but A shouldn't be overlooked. Protecting personal data is essential for fairness.
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Galen
17 days ago
C is definitely important for keeping the model up-to-date with new training data.
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Barney
24 days ago
I think B is also important. Monitoring for data drift can help maintain accuracy.
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Carolynn
30 days ago
I agree, but D is crucial too. Optimizing resources ensures efficiency.
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Tyisha
1 months ago
I think B is also important. Monitoring for data drift can help maintain accuracy.
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Magda
2 months ago
Definitely B! Monitoring for data drift is crucial to maintain fairness and accuracy in a deployed system.
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Micheline
30 days ago
Protecting against loss of personal data in the model is crucial for maintaining fairness as well.
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Lanie
1 months ago
Optimizing computational resources and data is key to ensure efficiency and scalability.
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Lashawnda
1 months ago
I think detecting anomalies outside established metrics is also important for maintaining fairness.
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Albert
1 months ago
I agree, monitoring for data drift is essential to ensure fairness.
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Tiara
2 months ago
I think it's important to monitor for data drift.
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