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Snowflake Exam DSA-C02 Topic 1 Question 22 Discussion

Actual exam question for Snowflake's DSA-C02 exam
Question #: 22
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 Catherin at Jul 04, 2024, 12:12 PM

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Tabetha
8 months ago
Option B, really? That's like trying to fix a broken leg by cutting off the whole leg. Gotta address the root cause, not just hide it.
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Carmelina
7 months ago
Option B, really? That's like trying to fix a broken leg by cutting off the whole leg. Gotta address the root cause, not just hide it.
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Dominga
7 months ago
C) Compare disparity between selection rates and performance metrics across ethnicities.
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Julio
7 months ago
A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
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Arlyne
8 months ago
Haha, option D - 'None of the above' - that's the easy way out. Where's the challenge in that?
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Heike
7 months ago
I agree with Heike. Removing the ethnicity feature from the training dataset is not a sustainable solution.
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Beula
7 months ago
Beula, that's a good point. But we should also compare disparity between selection rates and performance metrics across ethnicities to ensure fairness.
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Elliott
7 months ago
I think we should evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
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Valentin
7 months ago
Haha, option D - 'None of the above' - that's the easy way out. Where's the challenge in that?
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Edmond
8 months ago
C) Compare disparity between selection rates and performance metrics across ethnicities.
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Nathalie
8 months ago
A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
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Lou
8 months ago
I think evaluating each trained model with a validation dataset and using the model with the highest accuracy score is the best approach.
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Earlean
8 months ago
Removing the ethnicity feature? That's like trying to ignore the elephant in the room. We need to face this head-on, not hide from it.
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Lavonne
8 months ago
Option C is the way to go! Checking for disparities across ethnicities is crucial to ensuring fairness. Accuracy alone doesn't cut it.
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Julene
8 months ago
Agreed, accuracy is not enough. We have to consider fairness and equality in our decision-making process.
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Ira
8 months ago
Option C is definitely important. We need to make sure there is no bias in our model.
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Sang
8 months ago
But wouldn't removing the ethnicity feature from the training dataset also help in reducing discrimination?
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Donette
8 months ago
I agree with Kattie. It's important to ensure fairness in the model.
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Kattie
9 months ago
I think we should compare disparity between selection rates and performance metrics across ethnicities.
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