CertNexus AIP-210 Exam - 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

Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.

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

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Glory

3 months ago

B is tricky, but I see the point.

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Abel

3 months ago

I disagree, D is not a fit for semi-supervised learning.

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Owen

4 months ago

Wait, so we use semi-supervised when we have a lot of unlabeled data? That’s surprising!

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Kattie

4 months ago

I think A and C are better choices.

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Laila

4 months ago

Definitely C and E! Makes total sense.

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Buck

4 months ago

I think option A could be right, but I also recall something about bias in labeled data being a concern, so maybe B is also a possibility.

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Lakeesha

5 months ago

I practiced a question like this, and I feel like option C is definitely a reason to use semi-supervised learning, but I'm torn about the second choice.

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Tess

5 months ago

I'm not entirely sure, but I think option E makes sense because it mentions having a lot of unlabeled data, which is a key part of semi-supervised learning.

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Willetta

5 months ago

I remember that semi-supervised learning is useful when we have limited labeled data, so I think options A and C might be correct.

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Yong

5 months ago

I've got this! Semi-supervised learning is perfect when you have a lot of unlabeled data (option E) but only a small amount of labeled data (options A and B). That's the sweet spot.

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Chantay

5 months ago

Okay, let's see. I think options A and B are the ones to go for here. The key is having a small set of labeled data that isn't fully representative.

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Therese

5 months ago

Hmm, this seems like a tricky one. I'll need to think carefully about when semi-supervised learning would be appropriate.

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Celia

5 months ago

I'm a bit confused on the difference between options C and E. Aren't they both about having limited labeled data? I'll need to re-read the question.

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Trinidad

5 months ago

Ah, value stream mapping, I remember learning about this in class. I'd say option A is the best answer - it's all about identifying waste and redundancy in the process.

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Kanisha

5 months ago

Okay, let's see here. Disk cleanup sounds like the right approach, but I want to double-check the other options just to be sure I'm not missing something. I don't want to rush into an answer and get it wrong.

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Glory

5 months ago

I'm a little unsure about the "Need for Security Server" option. Does that mean turning it on or off? I'll have to double-check that one to make sure I understand it correctly.

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Lisbeth

10 months 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

10 months ago

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

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Yvonne

10 months ago

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

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Nobuko

10 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

10 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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Adelaide

9 months ago

Definitely A and C. We got this!

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Erasmo

9 months ago

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

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Nilsa

10 months ago

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

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Pura

11 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

10 months ago

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

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King

10 months ago

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

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Fernanda

11 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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