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]

Which method is used for detecting data outliers in Machine learning?

AScaler

BZ-Score

CBOXI

DCMIYC

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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Lashawn

2 months ago

B) Z-Score is the way to go. Anything else and you'll be z-scoring your way out of this exam!

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Yuonne

18 days ago

B) Yeah, Z-Score is the way to go. It's a reliable method for sure.

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Yolande

20 days ago

A) Scaler might work too, but Z-Score is more commonly used.

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Deonna

25 days ago

B) Z-Score is definitely the best method for detecting outliers.

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Fabiola

2 months ago

Haha, I thought D) CMIYC stood for 'Catch Me If You Can' at first. Gotta keep those outliers on their toes, you know!

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Delila

1 months ago

C) BOXI is not a method used for detecting data outliers.

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Alise

2 months ago

Haha, I thought D) CMIYC stood for 'Catch Me If You Can' at first. Gotta keep those outliers on their toes, you know!

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Barrett

2 months ago

B) Z-Score is another method used for detecting data outliers.

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Louis

2 months ago

A) Scaler is used for detecting data outliers in Machine learning.

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Phillip

2 months ago

C) BOXI and D) CMIYC? Really? Those sound more like secret agent code names than ML techniques. Nice try, exam writer!

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Vicente

3 months ago

I was confused between A) Scaler and B) Z-Score, but Z-Score makes more sense for outlier detection.

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Caprice

2 months ago

Yes, Z-Score is a statistical method that measures how far a data point is from the mean in terms of standard deviations.

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Frederica

2 months ago

I agree, Z-Score is commonly used for detecting outliers in Machine learning.

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Alberta

3 months ago

B) Z-Score is the correct answer for detecting data outliers in Machine Learning. It's a standard technique to identify data points that deviate significantly from the mean.

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