Databricks Certified Professional Data Scientist Exam - Topic 2 Question 45 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam

Question #: 45
Topic #: 2

[All Databricks Certified Professional Data Scientist Questions]

Select the correct option which applies to L2 regularization

AComputational efficient due to having analytical solutions

BNon-sparse outputs

CNo feature selection

Show Suggested Answer

Suggested Answer: B

by Cecil at Aug 04, 2023, 08:38 AM

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Layla

3 months ago

Yeah, it just shrinks coefficients instead of eliminating them.

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Crista

3 months ago

It's definitely computationally efficient, but no analytical solutions here!

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Rebbecca

4 months ago

Wait, I thought it could help with feature selection?

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Jin

4 months ago

Totally agree, it keeps all features in the model.

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Bong

4 months ago

L2 regularization doesn't lead to sparse outputs.

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Roselle

4 months ago

I thought L2 was computationally efficient because it has a closed-form solution, but I might be mixing it up with something else.

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Percy

4 months ago

I feel like I read somewhere that L2 regularization doesn't perform feature selection, but I can't recall the exact reason why.

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Stephanie

5 months ago

I remember practicing a question about L2 regularization, and I think it does lead to non-sparse outputs since it doesn't eliminate features like L1 does.

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Ma

5 months ago

I think L2 regularization is supposed to help with overfitting, but I'm not sure about the computational efficiency part.

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Ricarda

5 months ago

Hmm, I'm a bit unsure about this one. I'll need to carefully read through the options and think about which ones represent actual benefits.

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Joaquin

5 months ago

Okay, let me think this through. Problem Management is about identifying and resolving the root causes of incidents, so I think the best answer is B - reducing the impact of preventable incidents.

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I feel pretty good about this question. The key decision-making entities in an enterprise are typically the higher-level management and leadership teams, which would fall under the "organizational structures" option. I'll go with that.

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Gilberto

5 months ago

Easy peasy! The answer is clearly A - comply with the regulations in each region. International standards don't supersede regional ones, so we need to make sure we're meeting all the local requirements.

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Richelle

10 months ago

I bet the person who wrote this question is just trying to trip us up. L2 regularization is like the vanilla ice cream of machine learning - it may not be fancy, but it gets the job done.

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Tresa

10 months ago

Wait, if L2 doesn't have analytical solutions, how am I supposed to finish this exam in time? I need to call in sick and binge-watch cat videos instead.

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Rosann

8 months ago

C) No feature selection

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Paz

8 months ago

B) Non-sparse outputs

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Theron

8 months ago

A) Computational efficient due to having analytical solutions

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Quentin

10 months ago

Aha! I think C is the correct answer. L2 regularization doesn't do feature selection, which is a bummer, but at least it's not computationally inefficient.

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Felix

8 months ago

C) No feature selection

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Marget

8 months ago

B) Non-sparse outputs

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Shawn

8 months ago

A) Computational efficient due to having analytical solutions

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Owen

8 months ago

Yes, L2 regularization doesn't do feature selection, but it's good that it's computationally efficient.

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Kate

8 months ago

C) No feature selection

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Martina

8 months ago

B) Non-sparse outputs

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Roy

9 months ago

A) Computational efficient due to having analytical solutions

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Odette

9 months ago

I agree with you, C is the correct answer for L2 regularization.

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Lauran

9 months ago

C) No feature selection

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Kenny

9 months ago

B) Non-sparse outputs

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Lashandra

10 months ago

A) Computational efficient due to having analytical solutions

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Evan

10 months ago

Non-sparse outputs? That's not really what I want from my model. I need something that can help me with feature selection.

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Elouise

9 months ago

L2 regularization helps with feature selection

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Kristin

9 months ago

C) No feature selection

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Florencia

9 months ago

B) Non-sparse outputs

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Pilar

9 months ago

A) Computational efficient due to having analytical solutions

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Gladys

9 months ago

L2 regularization is not ideal for feature selection

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Maurine

10 months ago

C) No feature selection

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Marylou

10 months ago

B) Non-sparse outputs

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Evangelina

10 months ago

A) Computational efficient due to having analytical solutions

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Herminia

10 months ago

I believe it's option B) Non-sparse outputs because it helps in keeping all the features in the model.

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Cherelle

10 months ago

Hmm, I thought L2 regularization was supposed to be computationally efficient, but this question says it doesn't have analytical solutions. I'm a bit confused.

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