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Huawei Exam H13-311_V3.5 Topic 1 Question 14 Discussion

Actual exam question for Huawei's H13-311_V3.5 exam
Question #: 14
Topic #: 1
[All H13-311_V3.5 Questions]

Which of the following statements about datasets are true?

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Suggested Answer: A, D

Feedforward neural networks (FNNs) are networks where information moves in only one direction---forward---from the input nodes through hidden layers to the output nodes. Both fully-connected neural networks (where each neuron in one layer connects to every neuron in the next) and convolutional neural networks (CNNs) (which have a specific architecture for image data) are examples of feedforward networks.

However, recurrent neural networks (RNNs) and Boltzmann machines are not feedforward networks. RNNs include loops where information can be fed back into previous layers, and Boltzmann machines involve undirected connections between units, making them a form of a stochastic network rather than a feedforward structure.


by Delisa at Jan 11, 2025, 07:23 PM

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Heidy
1 months ago
I hope the test isn't as confusing as this question. Maybe they should just ask 'Which of these statements are true?' and let us figure it out.
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Janey
3 days ago
C) In machine learning, a dataset is generally divided into a training set, validation set, and test set.
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Nguyet
6 days ago
B) A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
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Tien
10 days ago
A) Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.
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Catherin
2 months ago
I'm just glad I don't have to remember all these terms on a daily basis. That's what Google is for, right?
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France
22 days ago
C) In machine learning, a dataset is generally divided into a training set, validation set, and test set.
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Yolando
2 months ago
B) A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
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Ailene
2 months ago
A) Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.
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Denise
2 months ago
D is incorrect. The validation set and test set serve different purposes in the machine learning process.
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Dana
1 months ago
D) When it comes to the machine learning process, the validation set and the test set are essentially the same.
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Dorthy
2 months ago
C) In machine learning, a dataset is generally divided into a training set, validation set, and test set.
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Alaine
2 months ago
B) A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
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Josphine
2 months ago
A) Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.
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Ashley
3 months ago
B is also correct. Datasets have multiple dimensions, and the events or attributes in each dimension are called features.
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Rikki
2 months ago
B) A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
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Felicitas
2 months ago
A) Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.
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Cecil
3 months ago
A and C are correct. Testing is indeed used to evaluate a trained model, and datasets are usually divided into training, validation, and test sets.
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Shanice
1 months ago
That's right. It's important to have separate sets for training, validation, and testing.
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Arlette
2 months ago
And datasets are usually divided into training, validation, and test sets.
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Ramonita
2 months ago
Yes, testing is used to evaluate a trained model.
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Frederic
2 months ago
I think A and C are correct.
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Noble
3 months ago
I believe statement C is true as well. Datasets are divided into training, validation, and test sets in machine learning.
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Elly
3 months ago
I agree with you, Asha. Statement B is also true because datasets have multiple dimensions.
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Asha
3 months ago
I think statement A is true because testing uses a trained model for prediction.
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