CompTIA DY0-001 Exam - 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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Isabella

7 months ago

Surprised to see input and output listed here, they don't downsample.

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Avery

7 months ago

Pooling is the right answer, no doubt about it!

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Edward

7 months ago

I agree, pooling layers are key in CNNs.

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Katie

7 months ago

Wait, isn't it the hidden layers that do the work?

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Wei

8 months ago

Definitely pooling! It's essential for downsampling.

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Stephaine

8 months ago

I feel like hidden layers are more about processing, but pooling layers seem to be the ones that actually downsample.

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Wade

8 months ago

I’m a bit confused about the different types of layers. Isn’t the input layer just where the data comes in?

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Alberto

8 months ago

I remember practicing a question like this where pooling was definitely the answer. It helps reduce dimensionality, right?

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Marva

9 months ago

I think pooling layers are used to downsample, but I’m not completely sure if it’s max pooling or average pooling that’s more common.

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Antione

9 months ago

I'm a bit confused on the difference between the input, output, and hidden layers in a CNN. I know pooling is used for downsampling, but I'm not sure if that's specifically for feature detection or just in general. I'll have to review my notes on CNN architecture before answering this.

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Thaddeus

9 months ago

Okay, let me walk through this step-by-step. Convolutional layers are used for feature detection, and pooling layers are used to downsample the feature maps. So the pooling layer is the one that handles the downsampling of the detected features. I'm confident that's the right answer.

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Herminia

9 months ago

Hmm, I'm not totally sure about this one. I know pooling is used for downsampling, but I can't remember if that's specifically for feature detection. I'll have to think this through a bit more.

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Gilbert

9 months ago

I'm pretty sure the answer is Pooling. That's the layer that's used to downsample the features in a CNN.

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Rocco

12 months 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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Tamie

11 months ago

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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Kerry

11 months ago

Pooling is the answer.

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Ling

1 year 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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Norah

11 months ago

It helps reduce the spatial dimensions of the input, making it more manageable for the network.

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Laurel

11 months ago

Yes, pooling is used to downsample feature detection in convolutional neural networks.

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Elke

12 months 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

1 year ago

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

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Hildegarde

1 year ago

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

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Lavonda

1 year ago

A) Pooling is used to downsample feature detection.

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Karma

1 year 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 year 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 year 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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