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?

AEvaluate each trained model with a validation dataset and use the model with the highest accuracy score.

BRemove the ethnicity feature from the training dataset.

CCompare disparity between selection rates and performance metrics across ethnicities.

DNone of the above.

Show Suggested 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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5 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

4 months ago

B) Remove the ethnicity feature from the training dataset.

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Yaeko

4 months ago

C) Compare disparity between selection rates and performance metrics across ethnicities.

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Maira

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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Rosalia

5 months ago

I think we should compare disparity between selection rates and performance metrics across ethnicities.

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Stanton

5 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

4 months ago

C) Compare disparity between selection rates and performance metrics across ethnicities.

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Amie

5 months ago

A) Evaluate each trained model with a validation dataset and use the model with the highest accuracy score.

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