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Huawei Exam H13-311_V3.5 Topic 5 Question 16 Discussion

Actual exam question for Huawei's H13-311_V3.5 exam
Question #: 16
Topic #: 5
[All H13-311_V3.5 Questions]

When learning the MindSpore framework, John learns how to use callbacks and wants to use it for AI model training. For which of the following scenarios can John use the callback?

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Suggested Answer: A, B, C, D

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by Lenora at Feb 07, 2025, 06:52 AM

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Viola
2 months ago
The answer is D. Callbacks are perfect for monitoring the training process, including loss values. Anything else would be a bit of a callback.
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Caprice
2 months ago
Callbacks? More like call-backs! Am I right? *crickets* Okay, okay, I'll see myself out. But seriously, option D is the way to go.
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Lashawn
2 months ago
Callbacks are so versatile! I can see how they could be used for all of these scenarios. But I'll have to go with option D to keep an eye on those loss values.
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Alise
2 months ago
Hmm, I'm not sure about adjusting activation functions with callbacks. That seems more like a model architecture concern. I'll go with option C for saving model parameters.
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Han
3 days ago
I believe option D is for monitoring loss values during training.
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Trinidad
13 days ago
I agree, option C is for saving model parameters.
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Stefanie
21 days ago
I see your point, option C for saving model parameters is definitely a useful application of callbacks.
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Noble
25 days ago
I think option A is correct for early stopping.
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Sena
1 months ago
I agree, option D is also a valid scenario for using callbacks to monitor loss values.
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Diego
1 months ago
I think option A is correct, early stopping is a common use case for callbacks.
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Willis
3 months ago
I believe John can also use the callback for monitoring loss values during training.
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Anabel
3 months ago
I agree with Lavelle, saving model parameters is a common use case for callbacks in AI model training.
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Lavelle
3 months ago
I think John can use the callback for saving model parameters.
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Thea
3 months ago
Early stopping is definitely one of the main use cases for callbacks in model training. I'm going with option A.
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Gail
1 months ago
Yes, it helps to stop training when the model performance stops improving.
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Tran
2 months ago
I agree, early stopping is crucial for preventing overfitting.
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Lennie
3 months ago
I think option D is the correct answer. Callbacks are commonly used to monitor the training process, including loss values.
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Yuki
2 months ago
D) Monitoring loss values during training
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Monroe
2 months ago
C) Saving model parameters
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Sherell
2 months ago
B) Adjusting an activation function
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Cathrine
2 months ago
A) Early stopping
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