Databricks Certified Professional Data Scientist Exam - Topic 4 Question 68 Discussion

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

Question #: 68
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

Which analytical method is considered unsupervised?

may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?

ANaive Bayesian classifier

BDecision tree

CLinear regression

DK-means clustering
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data, possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing, medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified,
labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

Show Suggested Answer

Suggested Answer: C

by Harrison at Sep 09, 2024, 07:57 AM

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Kanisha

3 months ago

I thought linear regression was unsupervised, but I guess not!

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Mendy

3 months ago

Decision trees can be unsupervised too, right?

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Henriette

3 months ago

Wait, is K-means really the only unsupervised method here?

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Antonio

4 months ago

Totally agree, it's all about finding patterns without labels.

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Annice

4 months ago

K-means clustering is definitely unsupervised!

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Noe

4 months ago

I keep mixing up supervised and unsupervised methods. I thought linear regression was unsupervised, but now I’m not so sure.

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Nohemi

4 months ago

I practiced a similar question about clustering methods last week. K-means was definitely mentioned as an unsupervised technique.

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Suzi

4 months ago

I'm a bit unsure, but I remember that naive Bayesian classifiers are supervised. So, that rules out option A, right?

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Svetlana

5 months ago

I think unsupervised methods are those that don't rely on labeled data, like clustering. K-means seems to fit that description.

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Felicitas

5 months ago

Ugh, I'm drawing a blank on the different learning methods right now. Supervised, unsupervised - they all start to blend together after a while. I'm just going to take my best guess and go with D. K-means. Hopefully that's the right call, but if not, I'll learn from this experience.

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Shizue

5 months ago

Hmm, I'm a bit unsure about this one. Supervised vs. unsupervised learning can be tricky to keep straight sometimes. Let me think this through - I know k-means is a clustering algorithm, so that seems like the most likely unsupervised option here. But I'll double-check the other choices just to be sure.

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Colette

5 months ago

This looks like a straightforward question on unsupervised learning methods. I'm pretty confident that the answer is D. K-means clustering is a classic unsupervised technique, so that's the one I'll go with.

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Vivan

5 months ago

Okay, I've got a strategy for this. I'm going to eliminate the supervised methods first - that rules out A. Naive Bayes and C. Linear regression. Then it's just a matter of deciding between B. Decision tree and D. K-means. Since k-means is explicitly called out as an unsupervised technique, I think that's the safest bet.

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Mose

5 months ago

This is a tricky one. I'm not totally clear on the differences between the various virtual routing options. I'll have to review my study materials and try to eliminate the incorrect answers.

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Phyliss

5 months ago

I remember discussions in class about how a two-part transfer pricing system can improve communication, which makes me lean towards option B as not being an advantage.

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Helga

10 months ago

Bingo! K-means is the way to go. Now, if only I could remember the optimal value for the number of clusters. I bet the exam writers are trying to trip us up with that one.

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Farrah

9 months ago

D) K-means clustering

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Vilma

10 months ago

C) Linear regression

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Bette

10 months ago

B) Decision tree

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Lisbeth

10 months ago

A) Naive Bayesian classifier

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Charolette

10 months ago

K-means clustering all the way! I can practically hear the cluster centroids moving around in my head. On a side note, I wonder if the exam proctors accept knock-knock jokes as partial credit...

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Hubert

8 months ago

D: Haha, I don't think knock-knock jokes will help with the exam, but k-means clustering sure will!

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Tina

9 months ago

C: I agree, it's a popular choice for clustering in various fields like marketing and image processing.

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Thaddeus

9 months ago

B: Absolutely! It's great for discovering hidden structures in the data.

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Kati

10 months ago

A: K-means clustering is definitely a powerful method for data analysis.

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Cristal

10 months ago

Hmm, I was torn between k-means and linear regression, but k-means makes the most sense for an unsupervised method. Maybe I should have paid more attention in my statistics class.

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Socorro

10 months ago

Yeah, k-means clustering is a common unsupervised method used for data analysis.

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Michel

10 months ago

Don't worry, clustering can be a bit tricky to grasp at first.

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Robt

11 months ago

Linear regression is supervised, so it can't be the answer. K-means clustering makes sense for unsupervised methods.

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Viola

11 months ago

K-means clustering is definitely the unsupervised method here. The question mentions that it uses an iterative algorithm to minimize the sum of distances from each object to its cluster centroid, which is a classic k-means approach.

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