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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 26 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 26
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

  • A. Apply label encoding to the color categories. Automatically assign each color a unique integer.
  • B. Implement padding to ensure that all color feature vectors have the same length.
  • C. Perform dimensionality reduction on the color categories.
  • D. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
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Suggested Answer: D 🗳️
by GiorgioGss at Nov. 27, 2024, 9:19 p.m.
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Saransundar
Highly Voted 5 months ago
Selected Answer: D
1. Label Encoding: Ordinal relationship 2. Padding: Sequence data 3. Dimensionality Reduction: High-dimensional data 4. One-Hot Encoding: Categorical data (Right)
upvoted 7 times
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Sadrik
Most Recent 1 month ago
Selected Answer: D
Label encoding is not ideal for categorical data in neural networks because the model might assume a numerical relationship (e.g., Blue is twice more important than Red), which is incorrect.
upvoted 1 times
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Sid4ops
1 month, 2 weeks ago
Selected Answer: D
Categorical data one hot always
upvoted 1 times
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GiorgioGss
5 months, 1 week ago
Selected Answer: D
One-hot encoding creates a new binary feature for each unique category (color in this case). For example, if there are three colors (red, blue, green), one-hot encoding would create three binary columns like this: Red: [1, 0, 0] Blue: [0, 1, 0] Green: [0, 0, 1] this way, the model can work with the color feature without assuming ordinal relationships between colors.
upvoted 2 times
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