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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 80 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 80
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?

  • A. Increase the learning rate.
  • B. Remove some irrelevant features from the training dataset.
  • C. Increase the value of the max_depth hyperparameter.
  • D. Decrease the value of the max_depth hyperparameter.
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Suggested Answer: D 🗳️
by GiorgioGss at Nov. 28, 2024, 5:39 p.m.
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Saransundar
3 months ago
Selected Answer: D
This is the scenario of overfitting where it works well with trained data and not with new data. Reducing the max_depth hyperparameter makes the model less complex, helping it generalize better to new data.
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