Snowflake DSA-C02 Exam - 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

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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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Shonda

9 months ago

CMIYC? Sounds made up, is that even a real method?

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Chantay

9 months ago

Scalers are not for outlier detection, just normalization!

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Barabara

9 months ago

Wait, what's BOXI? Never heard of that one.

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Amie

10 months ago

Totally agree, Z-Score is reliable!

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Casey

10 months ago

Z-Score is a common method for outlier detection.

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Gwen

10 months ago

I’m leaning towards Z-Score too; it’s like a standard way to identify outliers based on standard deviations, right?

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Shalon

10 months ago

BOXI sounds familiar, but I can't recall if it's actually a method for outlier detection or something else.

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Thea

10 months ago

I remember practicing with a question about outliers, and I feel like Scaler was mentioned, but it doesn't seem right for this.

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Dorathy

11 months ago

I think Z-Score is the method we learned for detecting outliers, but I'm not completely sure if it's the only one.

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Glory

11 months ago

Ah, I remember this now! The Z-Score method is definitely the way to go for detecting outliers in machine learning datasets. Glad I brushed up on that before the exam.

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Gary

11 months ago

Okay, let me see. I think the Z-Score method is used to identify outliers by calculating the standard deviation from the mean. That sounds right, but I'll double-check my notes just to be sure.

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Mozell

11 months ago

I'm pretty sure the answer is Z-Score, that's the method we learned for detecting outliers in the machine learning module.

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Hoa

11 months ago

Hmm, I'm a bit unsure on this one. I know we covered outlier detection, but I can't quite remember the specific method. I'll have to think this through carefully.

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Sarina

11 months ago

I'm a bit confused on this one. The options seem to cover different aspects of the TOGAF process, but I'm not sure which one specifically relates to the Preliminary Phase.

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Lashawn

1 year 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

1 year ago

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

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Yolande

1 year ago

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

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Deonna

1 year ago

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

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Fabiola

1 year 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 year ago

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

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Alise

1 year 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

1 year ago

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

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Louis

1 year ago

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

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Phillip

1 year 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

1 year 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

1 year 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

1 year ago

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

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Alberta

1 year 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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