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Snowflake DSA-C02 Exam - Topic 1 Question 3 Discussion

You are training a binary classification model to support admission approval decisions for a college degree program.How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?
C) Compare disparity between selection rates and performance metrics across ethnicities.
A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
B) Remove the ethnicity feature from the training dataset.
D) None of the above.

Snowflake DSA-C02 Exam - Topic 1 Question 3 Discussion

Actual exam question for Snowflake's DSA-C02 exam
Question #: 3
Topic #: 1
[All DSA-C02 Questions]

You are training a binary classification model to support admission approval decisions for a college degree program.

How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?

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Suggested Answer: C

By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.


by Andra at Oct 02, 2023, 12:47 PM

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Karan
8 months ago
D seems like a cop-out. We need better options!
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Serina
8 months ago
Wait, removing ethnicity might hide issues, right?
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Reena
8 months ago
A is not enough, accuracy doesn't mean fairness.
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Eva
8 months ago
Totally agree with C! Disparity analysis is key.
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Larae
8 months ago
C is the way to go! Gotta check those selection rates.
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Lettie
9 months ago
I’m a bit confused about this one. I feel like none of the options fully address the fairness issue.
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Ashton
9 months ago
Comparing selection rates across different ethnicities sounds familiar; I think that might be the right approach to ensure fairness.
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Micah
9 months ago
I think removing the ethnicity feature might help, but it feels like it could just mask the problem instead of solving it.
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Buck
9 months ago
I remember we discussed evaluating models for fairness, but I'm not sure if just looking at accuracy is enough.
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Ora
9 months ago
Ah, I see. Comparing the selection rates and performance across ethnicities is probably the way to go to really assess if the model is fair. That's the approach I'll focus on.
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Pamella
9 months ago
I'm a bit confused on this one. I'll need to review the concepts of model fairness and discrimination more carefully before deciding.
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Jose
9 months ago
Removing the ethnicity feature seems like the simplest solution, but I'm not sure if that's really the best way to evaluate fairness.
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Michael
9 months ago
Okay, I've got a few ideas here. I'm leaning towards comparing the performance metrics across different ethnicities to check for any unfair biases.
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Margurite
9 months ago
Hmm, this is a tricky one. I think I'll need to really think through the different approaches and their pros and cons.
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Wilbert
9 months ago
I'm a bit confused on this one. Is the first fetch supposed to be manually triggered? That's not something I was aware of, so I'll need to double-check that.
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Geraldine
9 months ago
This looks straightforward enough. I'm confident I can put together the right UPDATE statement to meet all the requirements. I'll just need to pay close attention to the details.
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Erinn
10 months ago
Okay, I've got a strategy in mind. I'll analyze the question, consider the Agile Scrum framework, and then select the option that seems most likely to drive a successful transformation.
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