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PMI CPMAI_v7 Exam - Topic 1 Question 4 Discussion

Actual exam question for PMI's CPMAI_v7 exam
Question #: 4
Topic #: 1
[All CPMAI_v7 Questions]

Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?

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Suggested Answer: C

by Chantay at Jun 14, 2025, 09:21 AM

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Glory
2 months ago
I doubt SVMs are the worst choice here, they have their uses!
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Meaghan
2 months ago
Wait, are we sure Gaussian Mixture is that bad?
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Lamonica
2 months ago
Naive Bayes is usually quick and cheap though.
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Rebeca
3 months ago
SVMs can be really slow to train on large datasets.
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Shelton
3 months ago
Totally agree, Neural Networks are super complex and costly!
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Erasmo
3 months ago
Gaussian Mixture models can be tricky too, but I feel like they might not be as costly as neural networks.
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Jame
3 months ago
Naive Bayes seems like a simpler option, but I’m not sure if it’s the best for all scenarios.
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Joye
4 months ago
I think neural networks might be overkill for projects with tight constraints on training time and cost.
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Jennifer
4 months ago
I remember that SVMs can be quite complex and take longer to train, especially with large datasets.
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William
4 months ago
Gaussian Mixture models seem like the way to go here. They're relatively simple to train and don't require as much data as some of the other algorithms. Plus, the training time and cost should be manageable for this project.
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Rosalia
4 months ago
I'm a bit confused by the question. Is Naive Bayes really that much better than the other options in terms of training time and complexity? I'll have to review my notes on the different algorithms to make sure I'm choosing the right one.
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Lorita
4 months ago
I'm pretty confident that Support Vector Machines (SVM) are the best option in this case. They're generally faster to train and less complex than neural networks, which should fit the constraints they've outlined.
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Denny
5 months ago
Okay, I think I've got this. Given the focus on training time, cost, and complexity, I'd say Neural Networks are probably the worst choice here. They tend to be more computationally intensive and require a lot of data to train effectively.
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Dortha
5 months ago
Hmm, this is a tricky one. I'm not sure if I fully understand the constraints they're asking about. I'll have to think this through carefully.
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Juliana
8 months ago
Hmm, Naive Bayes, eh? Sounds like the perfect algorithm for my 'Guess the Instructor's Age' project.
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Wilson
7 months ago
User 3: I agree with Mattie, Naive Bayes is simple and effective for certain tasks.
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Mattie
7 months ago
User 2: Really? I was leaning towards Support Vector Machines.
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Dwight
7 months ago
User 1: I think Naive Bayes would be a good choice for your project.
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Jani
8 months ago
Gaussian Mixture? Sounds like a tasty beverage, not a machine learning algorithm. I'll pass on that one.
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Marvel
7 months ago
C) Naive Bayes
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Mitsue
7 months ago
B) Neural Networks
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Maryanne
8 months ago
A) Support Vector Machines (SVM)
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Art
8 months ago
Neural Networks? More like Neural Nightmares! The training time alone would make me age a decade.
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Alyssa
8 months ago
Definitely not SVM, that thing is a computational beast! I'd go with Naive Bayes - simple and efficient, just what we need.
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Narcisa
7 months ago
Neural Networks could be too complex and costly for this project, I would avoid it.
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Tamra
7 months ago
I think Gaussian Mixture might also be a good option, it can handle complex data distributions.
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Colene
8 months ago
Naive Bayes is definitely a good choice for this project, it's lightweight and easy to implement.
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Kate
8 months ago
I agree, SVM can be really slow and resource-intensive.
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Miss
8 months ago
I would go with Naive Bayes. It is simpler and faster to train compared to Neural Networks, making it a better choice for this project.
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Lashandra
8 months ago
I agree with Essie. Neural Networks can be computationally expensive and may not be the most efficient option given the constraints.
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Essie
8 months ago
I think Neural Networks might not be the best choice because they can be complex and require a lot of training time.
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