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?

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 Catherin at Jul 04, 2024, 12:12 PM

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Tabetha

4 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

3 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

4 months ago

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

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Julio

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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Arlyne

5 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

3 months ago

I agree with Heike. Removing the ethnicity feature from the training dataset is not a sustainable solution.

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Beula

4 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

4 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

4 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

4 months ago

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

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Nathalie

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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Lou

5 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

5 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

5 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

4 months ago

Agreed, accuracy is not enough. We have to consider fairness and equality in our decision-making process.

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Ira

4 months ago

Option C is definitely important. We need to make sure there is no bias in our model.

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Sang

5 months ago

But wouldn't removing the ethnicity feature from the training dataset also help in reducing discrimination?

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Donette

5 months ago

I agree with Kattie. It's important to ensure fairness in the model.

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Kattie

6 months ago

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

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