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Google Professional Machine Learning Engineer Exam - Topic 2 Question 95 Discussion

You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
D) Convolutional Neural Networks (CNN)
A) Reinforcement learning
B) Recommender system
C) Recurrent Neural Networks (RNN)

Google Professional Machine Learning Engineer Exam - Topic 2 Question 95 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 95
Topic #: 2
[All Professional Machine Learning Engineer Questions]

You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

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Suggested Answer: D

by Dell at Jan 13, 2025, 09:25 PM

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Selene
9 months ago
Just a reminder, CNNs are designed for spatial data like images.
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Latricia
9 months ago
Wait, why not use reinforcement learning? Seems interesting.
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Matthew
10 months ago
CNNs are the best choice for this type of task!
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Kate
10 months ago
RNNs are not the right fit here, right?
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Kimbery
10 months ago
Definitely going with CNN for image processing!
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Desiree
10 months ago
Reinforcement learning seems off for this task since it's more about decision-making rather than image analysis. I lean towards CNNs too.
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Johnetta
11 months ago
I practiced a similar question about image classification, and I think CNNs were highlighted as the go-to method for feature extraction in images.
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Aleisha
11 months ago
I'm not entirely sure, but I feel like RNNs are more for sequential data, not images. So maybe that's not the best option?
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Odette
11 months ago
I remember studying that CNNs are specifically designed for image processing, so I think option D might be the right choice.
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Janae
11 months ago
Hmm, I'm not sure if Recurrent Neural Networks (RNN) are the best fit for this problem. They're usually better suited for sequential data, like text or time series. I think I'll go with Convolutional Neural Networks (CNN).
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Eleni
11 months ago
Convolutional Neural Networks (CNN) are definitely the way to go here. They're designed to handle image data and can extract relevant features with relatively low computational cost.
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Bernardine
11 months ago
I'm a bit unsure about this one. Reinforcement learning could work, but it might be overkill for a simple defect detection task. I'll have to think this through more carefully.
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Timothy
11 months ago
This seems like a classic computer vision problem, so I'd go with Convolutional Neural Networks (CNN). They're great at extracting features from images efficiently.
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Lisha
1 year ago
CNN is the way to go, but I'm also curious if they have any defective products that could be used as training data. You know, for a little 'quality control' practice.
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Ty
1 year ago
I heard CNN stands for 'Cats, Catnip, and Napping'. I'm pretty sure that's not what we're looking for in this case.
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Sean
1 year ago
Reinforcement learning? That's for training agents to take actions in an environment. I think you need to go back and review your machine learning fundamentals, my friend.
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Edison
1 year ago
Recommender systems? Really? I think you might be confusing this with a completely different problem. CNN is the clear choice here.
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Dacia
1 year ago
RNN might not be the most efficient choice for image feature extraction.
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Vivan
1 year ago
Recommender systems are not suitable for this problem.
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Justine
1 year ago
CNN is definitely the way to go for image processing.
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Lavonne
1 year ago
I think CNN is the best approach for this task.
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Rebecka
1 year ago
I was thinking RNN might work, but CNN is probably a better fit since we're dealing with images rather than sequential data.
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Ilene
1 year ago
Let's go with CNN for our model to identify defects in products based on images.
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Fallon
1 year ago
RNNs might work for sequential data, but CNNs are more suitable for image analysis.
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Pauline
1 year ago
CNNs are great at extracting features from images efficiently.
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Aide
1 year ago
I agree, CNN is definitely the way to go for image processing.
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Vashti
1 year ago
CNN is definitely the way to go here. It's designed to efficiently process and extract features from images, which is exactly what this problem requires.
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Lynna
1 year ago
CNNs are great for extracting features from images quickly and efficiently.
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Rolf
1 year ago
I agree, CNN is the best choice for image processing tasks.
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Micaela
1 year ago
I'm not sure about CNNs. Maybe we should consider Recurrent Neural Networks (RNN) as well for this task.
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Sueann
1 year ago
I agree with Annamae. CNNs are great for image processing tasks like identifying defects.
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Annamae
1 year ago
I think we should use Convolutional Neural Networks (CNN) for this task.
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