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CertNexus AIP-210 Exam - Topic 3 Question 17 Discussion

When should you use semi-supervised learning? (Select two.)
B) A small set of labeled data is biased toward one class.
A) A small set of labeled data is available but not representative of the entire distribution.
C) Labeling data is challenging and expensive.
D) There is a large amount of labeled data to be used for predictions.
E) There is a large amount of unlabeled data to be used for predictions.

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.)

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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
6 months ago
B is tricky, but I see the point.
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Abel
7 months ago
I disagree, D is not a fit for semi-supervised learning.
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Owen
7 months ago
Wait, so we use semi-supervised when we have a lot of unlabeled data? That’s surprising!
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Kattie
7 months ago
I think A and C are better choices.
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Laila
7 months ago
Definitely C and E! Makes total sense.
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Buck
7 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
8 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
8 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
8 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
8 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
8 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
8 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
8 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
8 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
8 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
8 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
1 year 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
1 year ago
E) There is a large amount of unlabeled data to be used for predictions.
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Yvonne
1 year ago
A) A small set of labeled data is available but not representative of the entire distribution.
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Nobuko
1 year 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 year 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
1 year ago
Definitely A and C. We got this!
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Erasmo
1 year ago
I agree, A and C make sense. Let's go with those.
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Nilsa
1 year ago
I think A and C are the right choices. Who needs representative data anyway?
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Pura
1 year 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
1 year ago
Yeah, biased datasets can be tricky. Semi-supervised learning is definitely useful when labeling data is tough.
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King
1 year ago
I think D is more for supervised learning too. B and C sound good to me.
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Fernanda
1 year 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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Sean
1 year ago
High-five to the person who wrote this question!
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Xuan
1 year ago
Semi-supervised learning is a great solution for these scenarios.
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Trevor
1 year ago
Definitely, using unlabeled data can be a game-changer.
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Krystal
1 year ago
I agree, labeling data can be really challenging.
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Graham
1 year ago
But what about when there is a large amount of unlabeled data available? Shouldn't we use semi-supervised learning then?
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Tatum
1 year ago
I agree with Marvel. It can help us make the most out of the limited labeled data we have.
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Marvel
1 year ago
I think we should use semi-supervised learning when labeling data is challenging and expensive.
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