CertNexus Exam AIP-210 Topic 3 Question 17 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 17
Topic #: 3

[All AIP-210 Questions]

When should you use semi-supervised learning? (Select two.)

AA small set of labeled data is available but not representative of the entire distribution.

BA small set of labeled data is biased toward one class.

CLabeling data is challenging and expensive.

DThere is a large amount of labeled data to be used for predictions.

EThere is a large amount of unlabeled data to be used for predictions.

Show Suggested Answer

Suggested Answer: B

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by Elenore at Apr 29, 2024, 04:17 PM

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Lisbeth

28 days ago

Wait, wait, wait... You're telling me I can use semi-supervised learning when I have a small set of labeled data AND a large amount of unlabeled data? Sounds like a recipe for 'Lazy Machine Learning: The Cookbook'.

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Flo

18 days ago

E) There is a large amount of unlabeled data to be used for predictions.

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Yvonne

20 days ago

A) A small set of labeled data is available but not representative of the entire distribution.

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Nobuko

1 months ago

Easy peasy! C and E are the way to go. Though I have to admit, I'm a little disappointed there's no option for 'All of the above and a free puppy'.

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Leatha

1 months ago

This is a tricky one, but I'm feeling confident. I'll go with A and C. Who needs representative data when you can just wing it, am I right?

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Erasmo

7 days ago

I agree, A and C make sense. Let's go with those.

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Nilsa

26 days ago

I think A and C are the right choices. Who needs representative data anyway?

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Pura

2 months ago

Hmm, I'm not sure about D. Isn't that more for supervised learning? I'm going with B and C - gotta love those biased datasets!

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Paola

23 days ago

Yeah, biased datasets can be tricky. Semi-supervised learning is definitely useful when labeling data is tough.

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King

26 days ago

I think D is more for supervised learning too. B and C sound good to me.

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Fernanda

2 months ago

I think the correct answers are C and E. Labeling data can be a pain, and using unlabeled data is a great way to leverage semi-supervised learning. High-five to the person who wrote this question!

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