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?

AProtect against loss of personal data in the model.

BMonitor for data drift that may affect performance and accuracy.

CDetect anomalies outside established metrics that require new training data.

DOptimize computational resources and data to ensure efficiency and scalability.

Show Suggested Answer

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

1 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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Kristofer

4 days ago

It's crucial to keep the model accurate and unbiased.

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Susy

14 days ago

Yeah, we need to constantly monitor and update the data to maintain fairness.

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Shalon

17 days ago

I agree, data drift can really mess up the system.

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Stevie

1 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

26 days ago

Protecting personal data is a priority to ensure privacy.

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Aliza

28 days 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

1 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

1 months ago

True, but A shouldn't be overlooked. Protecting personal data is essential for fairness.

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Galen

1 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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