CompTIA Exam DY0-001 Topic 1 Question 3 Discussion

Actual exam question for CompTIA's DY0-001 exam

Question #: 3
Topic #: 1

Which of the following types of layers is used to downsample feature detection when using a convolutional neural network?

APooling

BInput

COutput

DHidden

Show Suggested Answer

Suggested Answer: A

Pooling layers (such as max pooling or average pooling) reduce the spatial dimensions of the feature maps by summarizing local neighborhoods, effectively downsampling the detected features and controlling overfitting.

by Kenny at Jun 13, 2025, 12:16 AM

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Rocco

3 days ago

Pooling is the answer. It's the layer that does the downsizing, just like when you're trying to fit a skyscraper into a dollhouse.

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Ling

8 days ago

Pooling, for sure. It's the layer that does the downsampling, just like when you're trying to fit a whole orchestra into a shoebox.

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Katlyn

16 days ago

I think it's important to use pooling to prevent overfitting in convolutional neural networks.

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Hildegarde

22 days ago

I agree with Lavonda, pooling helps in reducing the dimensionality of the feature maps.

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Lavonda

1 months ago

A) Pooling is used to downsample feature detection.

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Karma

1 months ago

The answer has to be pooling. That's the layer that reduces the spatial dimensions, right? It's like when you're trying to fit a king-size bed into a studio apartment.

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Carlton

1 months ago

I'm pretty sure it's pooling. It's like taking all the features and condensing them into a more manageable size, kind of like when you're trying to fit a whole buffet onto a single plate.

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Mariann

1 months ago

Definitely pooling! Downsampling is like squeezing a lot of data into a smaller space, just like when you're trying to fit all your clothes in a suitcase.

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