BCS AIF Exam - Topic 8 Question 47 Discussion

Actual exam question for BCS's AIF exam

Question #: 47
Topic #: 8

With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what type of machine learning?

ABatch learning.

BBig Data learning.

CPatchwork learning.

DOnline learning.

Show Suggested Answer

Suggested Answer: D

Online learning is a type of machine learning that can be used when a large dataset is limited in computational resources or if the data is frequently changing. It allows the system to learn from new data as it is being presented, rather than having to re-train the entire dataset each time new data is added. This makes it more efficient and effective than batch learning, as it only needs to process the new data and not the entire dataset. Online learning is often used in applications such as fraud detection, where new data is constantly being added and needs to be analyzed quickly.

For more information, please refer to the BCS Foundation Certificate In Artificial Intelligence Study Guide (https://www.bcs.org/upload/pdf/bcs-foundation-certificate-in-artificial-intelligence-study-guide.pdf)or the EXIN Artificial Intelligence Foundation Certification (https://www.exin.com/en/exams/artificial-intelligence-foundation).

by Arlyne at Sep 18, 2024, 02:40 AM

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Eileen

5 months ago

Not sure if online learning is the best choice here...

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Fernanda

5 months ago

Online learning makes sense with new data coming in!

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Nilsa

6 months ago

Wait, is patchwork learning even a thing?

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Lashaunda

6 months ago

I thought it was batch learning?

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Alpha

6 months ago

Definitely online learning!

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Magda

6 months ago

Patchwork learning sounds familiar, but I don't recall it being a standard term in our studies.

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Valentin

6 months ago

I remember practicing a question about this, and I think online learning was the answer because it adapts to new data continuously.

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Alona

7 months ago

I'm not entirely sure, but batch learning seems like it could fit too, since it processes data in chunks.

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Weldon

7 months ago

I think we might be looking for online learning here, especially since it deals with frequent new data.

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Sonia

7 months ago

Hmm, I'm a bit stumped on this one. The question is asking about the type of machine learning we'd use, but the options don't seem super clear to me. I'll have to review my notes on the different learning approaches and see if I can figure out which one fits best with the constraints mentioned. Hopefully I can narrow it down.

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Marcos

7 months ago

Okay, I've got this. With a large dataset and limited resources, we can't really do batch learning since that requires loading the entire dataset into memory. And "big data learning" isn't a real thing, so that's not the answer. That leaves online learning as the best option - it can handle new data as it comes in without needing to retrain on the full dataset each time. I'm confident that's the right approach here.

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Aja

7 months ago

This seems like a straightforward question about different machine learning approaches. I think the key is to identify the approach that can handle large datasets, limited resources, and frequent new data - that sounds like online learning to me.

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Quentin

7 months ago

Hmm, I'm a bit unsure about this one. I know there are different learning approaches like batch and online, but I'm not totally clear on the differences and which one would be best for the scenario described. I'll have to think this through carefully.

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