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Databricks Certified Professional Data Scientist Exam - Topic 4 Question 61 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 61
Topic #: 4
[All Databricks Certified Professional Data Scientist Questions]

You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?

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Suggested Answer: C

by Whitney at Jun 03, 2024, 11:21 AM

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Kimbery
3 months ago
Linear regression? That's not even close to what we need here!
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Murray
3 months ago
Grouping makes sense, especially with different pizza types.
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Thad
3 months ago
Wait, are we really using K-means for pizza? Sounds odd!
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Vincenza
4 months ago
I think Naive Bayes would be better for this.
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Pearline
4 months ago
K-means clustering is definitely the way to go!
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Alpha
4 months ago
I feel like association rules could be relevant, but I can't recall if they apply to grouping like this.
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Viola
4 months ago
K-means sounds familiar, especially from practice questions where we had to cluster similar items.
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Tammi
4 months ago
I'm not entirely sure, but I remember something about Naive Bayes being used for classification, not grouping.
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Yen
5 months ago
I think we might need to use K-means clustering since we're grouping pizzas based on their properties like size and calories.
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Kirk
5 months ago
K-means clustering is the clear choice for this problem. I'll need to be careful with the feature selection and normalization to get good results.
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Dottie
5 months ago
The question is pretty straightforward. K-means clustering is definitely the way to go here. I'll need to preprocess the data, decide on the number of clusters, and then run the algorithm.
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Loise
5 months ago
I'm a bit unsure about this one. Is K-means the best technique, or would something like association rules be more appropriate for grouping the pizzas?
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Sherell
5 months ago
K-means clustering sounds like the right approach here. I'll need to determine the optimal number of clusters and then assign each pizza to the nearest cluster center.
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Floyd
5 months ago
This seems like a classic clustering problem. I'd go with K-means clustering to group the pizzas based on their properties.
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Dahlia
5 months ago
This is a tricky one, but I'm going to give it my best shot. I'll start by mapping out the stages in the order I think is correct.
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Regenia
5 months ago
I can't quite recall if we just subtract the interest earned from the loan interest or if there's more to it... I remember it being tricky.
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An
5 months ago
I practiced a similar question before, and I think the "permit ip any" option is often used as an example but doesn't seem secure enough for a DMZ setting.
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Loreta
5 months ago
From practice questions, I recall that heart rates going over 200 bpm could be a sign too, but it feels like there was more to it.
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Jacinta
2 years ago
I bet the person who came up with 'Grouping' as an answer was just really hungry and wanted to eat all the pizzas.
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Zana
2 years ago
C) K-means Clustering
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Wynell
2 years ago
A) Association Rules
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German
2 years ago
Haha, linear regression? For grouping pizzas? I'd like to see how that's supposed to work. K-means is the obvious answer.
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Lashon
2 years ago
Wait, why would you use a Naive Bayes classifier for this? That's more for classification, not grouping. K-means is the clear choice.
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Mattie
2 years ago
Using K-means Clustering, we can determine groups of pizzas with similar characteristics.
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Desiree
2 years ago
K-means Clustering is the best technique for grouping similar objects based on their properties.
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Lavonda
2 years ago
Naive Bayes Classifier is used for classification, not grouping.
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Cristy
2 years ago
I would go with Naive Bayes Classifier, it might work well too.
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Stevie
2 years ago
I agree with you, K-means Clustering is a good choice for this task.
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Carolann
2 years ago
I think I would use K-means Clustering for grouping the pizzas.
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Kizzy
2 years ago
K-means clustering is definitely the way to go here. Grouping similar pizzas based on their properties is exactly what the question is asking for.
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Stacey
2 years ago
Definitely, K-means clustering is the most suitable method for grouping pizzas with similar properties in this scenario.
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Burma
2 years ago
Yes, K-means clustering is perfect for this task. It helps in creating distinct groups based on similarities.
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Tanesha
2 years ago
I agree, K-means clustering is the best technique for grouping similar pizzas based on their properties.
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