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Snowflake Exam DSA-C02 Topic 2 Question 24 Discussion

Actual exam question for Snowflake's DSA-C02 exam
Question #: 24
Topic #: 2
[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?

Show Suggested Answer Hide Answer
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 Geraldo at Jul 18, 2024, 12:35 AM

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Rosalyn
4 months ago
C is the way to go. Gotta look at the selection rates and metrics for each ethnicity to ensure there's no discrimination happening. Anything less is just sloppy work.
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Shizue
4 months ago
Haha, option D - 'None of the above' - that's the easy way out. Real data scientists roll up their sleeves and tackle the tough questions of fairness and equity.
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Kimberlie
2 months ago
B) Remove the ethnicity feature from the training dataset.
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Edelmira
3 months ago
C) Compare disparity between selection rates and performance metrics across ethnicities.
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Felton
3 months ago
A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
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Sylvia
4 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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Gilbert
4 months ago
I agree with Rosalia, comparing disparity is important to ensure fairness and avoid discrimination.
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Maryanne
4 months ago
But wouldn't removing the ethnicity feature from the training dataset be a better option to ensure fairness?
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Truman
4 months ago
B is a cop-out, just removing the feature won't solve the underlying bias. Gotta dig deeper and actually analyze the model's performance across groups.
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Winfred
3 months ago
B) Remove the ethnicity feature from the training dataset.
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Yaeko
3 months ago
C) Compare disparity between selection rates and performance metrics across ethnicities.
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Maira
3 months ago
A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
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Rosalia
4 months ago
I think we should compare disparity between selection rates and performance metrics across ethnicities.
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Stanton
4 months ago
Option C seems legit, gotta check that model isn't biased against any ethnicities. Don't want to repeat the college admission scandals, am I right?
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Katheryn
3 months ago
C) Compare disparity between selection rates and performance metrics across ethnicities.
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Amie
4 months ago
A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.
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