Databricks Certified Professional Data Scientist Exam - Topic 2 Question 12 Discussion

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

Question #: 12
Topic #: 2

[All Databricks Certified Professional Data Scientist Questions]

Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

ASemi Linear Regression

BLogistic regression

CNaive Bayesian classification

DLinear regression

EK-means clustering
k-means clustering is a method of vector quantization^ originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
The algorithm has nothing to do with and should not be confused with k-nearest neighbor another popular machine learning technique.

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Suggested Answer: E

by Wilda at May 06, 2022, 05:49 AM

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Audry

4 months ago

I thought linear regression could be used for this too?

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Catarina

4 months ago

Logistic regression won't work without labels.

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Angelo

4 months ago

Wait, are we sure K-means is the best option?

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Tamesha

4 months ago

Totally agree, clustering is the way to go here.

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Alaine

5 months ago

K-means clustering is great for unlabeled data!

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Linn

5 months ago

I’m a bit confused about the options. I thought linear regression was for predicting continuous outcomes, so it can't be the right answer here, can it?

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Gaynell

5 months ago

I remember practicing with similar questions, and I feel like logistic regression is for labeled data. So, it can't be that one, right?

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Shannon

5 months ago

I think we might need to use a clustering method since the records are unlabeled. K-means sounds familiar, but I'm not entirely sure if it's the best choice.

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Nguyet

5 months ago

K-means clustering seems to fit the scenario since we have three groups. But what if the data has different shapes? Would that affect our choice?

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Mozell

5 months ago

I'm a bit confused by this question. I'm not entirely familiar with the Vault installation process, so I'll have to review the materials again to be sure.

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Jesusita

5 months ago

I'm a bit confused on this one. I know we use $(( )) for arithmetic, but I'm not sure about the other options. I'll have to review my Bash scripting notes to make sure I understand the different ways to evaluate expressions.

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Willodean

5 months ago

Option D sounds like it could be the right answer. Black-box testing is about testing the interactions and interfaces between different components, right? I'll make sure to double-check the definitions to be sure.

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