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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
2 months 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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Fallon
4 days ago
Definitely, we can't let the system become outdated and biased.
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Kristofer
9 days ago
It's crucial to keep the model accurate and unbiased.
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Susy
19 days ago
Yeah, we need to constantly monitor and update the data to maintain fairness.
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Shalon
22 days ago
I agree, data drift can really mess up the system.
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Stevie
2 months 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
2 months 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
2 months 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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Brandon
1 months ago
Protecting personal data is a priority to ensure privacy.
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Aliza
1 months ago
Optimizing computational resources is essential for efficiency.
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Noel
1 months ago
I think monitoring for data drift is also important to maintain accuracy.
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Sueann
1 months ago
I agree, detecting anomalies is crucial for fairness in the system.
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Kirk
2 months 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
2 months 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
1 months ago
Absolutely, we need to stay on top of any anomalies to keep the system running smoothly.
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Kenny
1 months ago
C) Detect anomalies outside established metrics that require new training data.
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Matthew
2 months ago
B) Monitor for data drift that may affect performance and accuracy.
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Jamey
2 months ago
I believe optimizing computational resources is key for fairness.
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Sage
2 months ago
I agree with Tiara, data drift can really impact performance.
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Jesse
2 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
2 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
1 months ago
C is definitely important for keeping the model updated with new training data.
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Sherly
1 months ago
I agree, but D is crucial too. Optimizing resources ensures efficiency in the system.
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Rochell
2 months ago
True, but A shouldn't be overlooked. Protecting personal data is essential for fairness.
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Galen
2 months ago
C is definitely important for keeping the model up-to-date with new training data.
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Barney
2 months ago
I think B is also important. Monitoring for data drift can help maintain accuracy.
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Carolynn
2 months ago
I agree, but D is crucial too. Optimizing resources ensures efficiency.
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Tyisha
2 months ago
I think B is also important. Monitoring for data drift can help maintain accuracy.
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Magda
3 months ago
Definitely B! Monitoring for data drift is crucial to maintain fairness and accuracy in a deployed system.
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Micheline
2 months ago
Protecting against loss of personal data in the model is crucial for maintaining fairness as well.
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Lanie
2 months ago
Optimizing computational resources and data is key to ensure efficiency and scalability.
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Lashawnda
2 months ago
I think detecting anomalies outside established metrics is also important for maintaining fairness.
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Albert
2 months ago
I agree, monitoring for data drift is essential to ensure fairness.
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Tiara
3 months ago
I think it's important to monitor for data drift.
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