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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
10 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
8 months ago
Definitely, we can't let the system become outdated and biased.
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Kristofer
9 months ago
It's crucial to keep the model accurate and unbiased.
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Susy
9 months ago
Yeah, we need to constantly monitor and update the data to maintain fairness.
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Shalon
9 months ago
I agree, data drift can really mess up the system.
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Stevie
10 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
10 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
10 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
9 months ago
Protecting personal data is a priority to ensure privacy.
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Aliza
9 months ago
Optimizing computational resources is essential for efficiency.
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Noel
9 months ago
I think monitoring for data drift is also important to maintain accuracy.
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Sueann
10 months ago
I agree, detecting anomalies is crucial for fairness in the system.
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Kirk
10 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
10 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
10 months ago
Absolutely, we need to stay on top of any anomalies to keep the system running smoothly.
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Kenny
10 months ago
C) Detect anomalies outside established metrics that require new training data.
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Matthew
10 months ago
B) Monitor for data drift that may affect performance and accuracy.
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Jamey
10 months ago
I believe optimizing computational resources is key for fairness.
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Sage
11 months ago
I agree with Tiara, data drift can really impact performance.
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Jesse
11 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
11 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
10 months ago
C is definitely important for keeping the model updated with new training data.
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Sherly
10 months ago
I agree, but D is crucial too. Optimizing resources ensures efficiency in the system.
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Rochell
10 months ago
True, but A shouldn't be overlooked. Protecting personal data is essential for fairness.
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Galen
10 months ago
C is definitely important for keeping the model up-to-date with new training data.
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Barney
10 months ago
I think B is also important. Monitoring for data drift can help maintain accuracy.
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Carolynn
10 months ago
I agree, but D is crucial too. Optimizing resources ensures efficiency.
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Tyisha
10 months ago
I think B is also important. Monitoring for data drift can help maintain accuracy.
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Magda
11 months ago
Definitely B! Monitoring for data drift is crucial to maintain fairness and accuracy in a deployed system.
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Micheline
10 months ago
Protecting against loss of personal data in the model is crucial for maintaining fairness as well.
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Lanie
10 months ago
Optimizing computational resources and data is key to ensure efficiency and scalability.
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Lashawnda
11 months ago
I think detecting anomalies outside established metrics is also important for maintaining fairness.
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Albert
11 months ago
I agree, monitoring for data drift is essential to ensure fairness.
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Tiara
11 months ago
I think it's important to monitor for data drift.
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