Free CompTIA DataX DY0-001 Actual Exam Questions - Question 12 Discussion

Errors going backward is key here, so D fits best. A’s about convolutions, which only apply to certain networks, so it’s too narrow. The others don’t really mention the core idea of error propagation.

Probably D. Backpropagation is all about sending the error signals backward through the network to adjust weights. A mentions convolutions, but that’s specific to CNNs and doesn’t capture the general idea. B talks about accuracy which isn’t really propagated backward, and C’s mention of nodes passing backward doesn’t fit the standard process. So D fits best by describing errors moving back to update parameters.

D imo because backpropagation specifically involves sending error gradients backward, not convolutions or nodes themselves. The other choices mix up concepts or are too narrow in scope.

B tbh doesn’t fit because accuracy isn’t something you pass backward; it’s a metric you calculate after. A talks about convolutions, which are specific to CNNs, so it’s too narrow for backpropagation in general. C’s mention of nodes going backward doesn’t make sense since it’s the error gradients moving back through connections, not the nodes themselves. D nails it by focusing on errors moving backward to update weights and biases, which is the core idea behind backpropagation.

D. The key point is that it’s the error that is propagated backward, which is used to compute gradients for updating weights. The other choices confuse terms that aren't directly linked to backpropagation.

Totally agree, it’s all about the errors flowing back, so D.

Makes sense to go with D because backpropagation literally means propagating the error signal back through the network to update the parameters. The other options mix up terms like convolutions or accuracy, which aren’t related to how backprop works. So D fits perfectly here.

It’s D for sure. Backpropagation specifically deals with errors, not convolutions or accuracy. Passing errors backward allows the network to figure out how to tweak weights to reduce the overall loss. The other options mention things that don’t really flow backward like that in training, so they can be ruled out easily.

It’s D. Backpropagation is all about sending the errors backward to adjust weights and biases. The other options mention things like convolutions, accuracy, or nodes going backward, which don’t really fit what backpropagation actually does. It’s specifically the error signals that get propagated back through the network.

It’s D.