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Snowflake DSA-C02 Exam - Topic 4 Question 35 Discussion

Actual exam question for Snowflake's DSA-C02 exam
Question #: 35
Topic #: 4
[All DSA-C02 Questions]

Select the Correct Statements regarding Normalization?

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Suggested Answer: D

All are correct except a standard-only stream tracks row inserts only.

A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).


by Stefany at Dec 10, 2024, 01:02 PM

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Colene
3 months ago
Yeah, outliers can really skew the results in normalization.
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Major
3 months ago
Wait, normalization is affected by outliers? That’s surprising!
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Alyce
3 months ago
Scikit-Learn has a Normalizer, but not a RecommendedScaler.
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Oretha
4 months ago
I thought it was mean and standard deviation for scaling?
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Marylou
4 months ago
Normalization definitely uses min and max values!
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Leslie
4 months ago
I feel like normalization and standardization are often confused, but I do remember that normalization can be influenced by outliers.
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Izetta
4 months ago
I practiced a question similar to this, and I think normalization typically uses mean and standard deviation, but I might be mixing it up with standardization.
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Pearly
4 months ago
I think normalization is definitely affected by outliers, but I can't recall if Scikit-Learn has a specific transformer called RecommendedScaler.
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Loreta
5 months ago
I remember that normalization often involves scaling data, but I'm not sure if it's specifically about min and max values or something else.
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Erasmo
5 months ago
Wait, I'm confused. I thought normalization was about scaling the data, not just the model. I'll have to review my notes on this before answering.
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Merilyn
5 months ago
Okay, I think I've got this. Normalization uses mean and standard deviation, and Scikit-Learn has a transformer called StandardScaler that does this. I'll select options B and C.
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Arthur
5 months ago
Hmm, I'm a bit unsure about this. I remember something about normalization being affected by outliers, but I can't recall the details. I'll have to think this through carefully.
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Renea
5 months ago
I'm pretty confident about this one. I know normalization uses mean and standard deviation, not min and max values, so I'll select option B.
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Iola
10 months ago
Normalization? More like Abnormalization, am I right? *chuckles* Okay, I'll see myself out.
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Gertude
9 months ago
Normalization techniques are important for data preprocessing.
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Deangelo
9 months ago
C) Scikit-Learn provides a transformer RecommendedScaler for Normalization.
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Michell
9 months ago
A) Normalization technique uses minimum and max values for scaling of model.
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Annabelle
10 months ago
A? Really? Normalization has nothing to do with min and max values. Sounds like someone needs to go back to their machine learning basics.
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Mike
10 months ago
D) Normalization got affected by outliers.
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Shayne
10 months ago
C) Scikit-Learn provides a transformer RecommendedScaler for Normalization.
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Marquetta
10 months ago
B) Normalization technique uses mean and standard deviation for scaling of model.
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Jettie
10 months ago
D is definitely true. Normalization can be affected by outliers in the data, so we need to be careful about that.
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Edda
10 months ago
Option C is interesting, but I'm not sure if that's the correct answer. I'll have to double-check the documentation.
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Billye
10 months ago
I believe option D) Normalization got affected by outliers is also important to consider, as outliers can skew the normalization process.
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Annett
10 months ago
I think option B is the correct statement. Normalization uses mean and standard deviation for scaling the data.
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Veda
9 months ago
Yes, option B is the correct statement. Normalization technique uses mean and standard deviation for scaling of model.
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Theola
9 months ago
I agree, option B is correct. Normalization uses mean and standard deviation for scaling.
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Johnna
11 months ago
I agree with Narcisa, because using mean and standard deviation helps in maintaining the distribution of data.
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Narcisa
11 months ago
I think the correct statement is B) Normalization technique uses mean and standard deviation for scaling of model.
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