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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 23 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 23
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

  • A. Enable early stopping on the model.
  • B. Increase dropout in the layers.
  • C. Increase the number of layers.
  • D. Increase the number of neurons.
  • E. Investigate and reduce the sources of model bias.
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Suggested Answer: AB 🗳️
by GiorgioGss at Nov. 27, 2024, 9:05 p.m.
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Saransundar
Highly Voted 4 months, 3 weeks ago
Selected Answer: AB
The issue is overfitting. Soln:- A. Early stopping:- Stops training when validation performance declines B. Increase dropout:- reduces overfitting by randomly disabling neurons
upvoted 5 times
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GiorgioGss
Most Recent 5 months ago
Selected Answer: AB
"improves substantially at first and then degrades after a specific number of epochs." Clear sign to stop it early and to drop
upvoted 2 times
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