Databricks Certified Professional Data Scientist Exam - Topic 4 Question 52 Discussion

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

Question #: 52
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

Digit recognition, is an example of.....

AClassification

BClustering

CUnsupervised learning

DNone of the above
Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.

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Suggested Answer: A

by Lai at Jan 25, 2024, 09:42 PM

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Ceola

3 months ago

Totally agree, classification is the way to go!

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Delfina

3 months ago

Wait, so it's not clustering? That's surprising!

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Francine

4 months ago

Yeah, it's all about classifying digits.

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Linette

4 months ago

I thought it was unsupervised learning?

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Terrilyn

4 months ago

Definitely classification!

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Sarah

4 months ago

I wonder if it could be unsupervised learning too? But I feel like digit recognition usually has labeled data for training.

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Raymon

4 months ago

I practiced a question like this before, and I think it was also about supervised learning, which fits with classification.

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Ammie

4 months ago

I'm a bit unsure, but I remember something about clustering being more about grouping similar items without labels.

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Sherita

5 months ago

I think digit recognition is definitely a classification problem since we're trying to identify specific digits from images.

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Jesusita

5 months ago

I'm a little confused by the last option mentioning unsupervised learning. I'll need to double-check my understanding of the differences between supervised and unsupervised learning to make sure I select the right answer.

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Nilsa

5 months ago

Hmm, I'm a bit unsure about the difference between classification and clustering here. I'll need to think through the key characteristics of each to determine the best approach.

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Antione

5 months ago

This looks like a straightforward classification problem. I'll start by carefully reading the question and options to understand what it's asking.

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Tamekia

5 months ago

Ah, I see now. Digit recognition is a classic example of a classification task, where the goal is to assign each input (digit) to one of a predefined set of classes (the digits 0-9). I'm confident I can apply my knowledge of supervised learning to answer this.

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Gaynell

5 months ago

Okay, let me think this through step-by-step. I need to make sure I understand the workspace information and how the code is trying to access the "mi-protect" workspace. I'll go through it methodically.

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Elli

5 months ago

Hmm, I'm not sure. Collecting only the necessary data seems like the most straightforward way to minimize privacy risk, but I'll have to consider the other options as well.

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Launa

5 months ago

I've seen questions like this before. I think the best approach is to focus on the specific details about the VXLAN source port and how it impacts the overlay packet forwarding.

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Charlie

5 months ago

I remember something similar from practice questions, but the exact number escapes me. Could it really be as high as 400?

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Soledad

10 months ago

This makes perfect sense. Digit recognition is all about classifying the input image into one of the 10 possible digit classes. The supervised learning approach is the way to go, as we can provide the model with labeled examples to learn from.

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Bonita

10 months ago

Haha, I bet the computer is better at recognizing digits than I am. Wouldn't it be funny if we had a digit recognition contest between humans and machines? I wonder who would win.

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Laura

9 months ago

A: It's amazing how advanced technology has become in tasks like digit recognition.

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Alease

9 months ago

B: Yeah, machines are usually more accurate and faster at recognizing digits.

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Wai

9 months ago

A: I think the computer would definitely win in a digit recognition contest.

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Rebeca

10 months ago

Hmm, I was a bit unsure at first, but the explanation really clears things up. Classification is indeed the way to go for digit recognition, as we have a set of known classes (the digits 0-9) that the model needs to learn to identify.

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Gayla

10 months ago

I agree, supervised learning is commonly used in classification tasks like digit recognition.

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Glen

10 months ago

I think it's definitely classification because we are trying to categorize digits into different classes.

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Sabra

11 months ago

Digit recognition is an example of classification.

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Launa

11 months ago

Ah, I see! Digit recognition is a classic example of classification, where we train a model to categorize input data into predefined classes. This is a great way to showcase supervised learning in action.

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