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Google Exam Professional Machine Learning Engineer 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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by Dell at Jan 13, 2025, 09:25 PM

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Lisha
29 days 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
30 days 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 months 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 months 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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Vivan
2 days ago
Recommender systems are not suitable for this problem.
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Justine
9 days ago
CNN is definitely the way to go for image processing.
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Lavonne
17 days ago
I think CNN is the best approach for this task.
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Rebecka
2 months 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 months ago
Let's go with CNN for our model to identify defects in products based on images.
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Fallon
1 months ago
RNNs might work for sequential data, but CNNs are more suitable for image analysis.
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Pauline
1 months ago
CNNs are great at extracting features from images efficiently.
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Aide
1 months ago
I agree, CNN is definitely the way to go for image processing.
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Vashti
2 months 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 months ago
CNNs are great for extracting features from images quickly and efficiently.
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Rolf
1 months ago
I agree, CNN is the best choice for image processing tasks.
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Micaela
2 months 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
2 months ago
I agree with Annamae. CNNs are great for image processing tasks like identifying defects.
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Annamae
3 months ago
I think we should use Convolutional Neural Networks (CNN) for this task.
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