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CompTIA Exam DY0-001 Topic 1 Question 5 Discussion

Actual exam question for CompTIA's DY0-001 exam
Question #: 5
Topic #: 1
[All DY0-001 Questions]

A data scientist is clustering a data set but does not want to specify the number of clusters present. Which of the following algorithms should the data scientist use?

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Suggested Answer: A

DBSCAN discovers clusters based on density without requiring you to predefine the number of clusters, automatically finding arbitrarily shaped groups and identifying noise points.


by Carey at Jun 12, 2025, 03:53 AM

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Desiree
8 days ago
As a data scientist, I'd rather not 'k-means' my way through this problem. DBSCAN is the way to 'cluster' the competition.
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Sherell
9 days ago
But DBSCAN is specifically designed for clustering without specifying the number of clusters.
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Alton
9 days ago
Hmm, logistic regression? That's for classification, not clustering. Clearly, the data scientist needs to brush up on their unsupervised learning algorithms.
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Yolando
16 hours ago
B: I agree, DBSCAN is a good choice for clustering without specifying the number of clusters.
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Lucy
2 days ago
A: I think the data scientist should use DBSCAN, it doesn't require specifying the number of clusters.
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Vanesa
10 days ago
I disagree, I believe k-nearest neighbors would be a better choice.
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Solange
16 days ago
k-means is a classic, but if the data has varying density clusters, DBSCAN is the better choice. Gotta love that density-based clustering!
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Emerson
21 days ago
DBSCAN is the way to go for this task! It's great for identifying clusters without needing to specify the number of clusters upfront.
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Shantay
6 days ago
DBSCAN is a great algorithm for this task, it's very flexible.
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Shawna
7 days ago
I agree, DBSCAN is perfect for clustering without specifying the number of clusters.
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Carmela
9 days ago
DBSCAN is definitely the best choice for this situation.
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Sherell
1 months ago
I think the data scientist should use DBSCAN.
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