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.

Show Suggested Answer

Suggested Answer: D 🗳️

by GiorgioGss at Nov. 27, 2024, 9:19 p.m.

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Saransundar

Highly Voted 6 months, 4 weeks 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 3 months 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

3 months, 2 weeks ago

Selected Answer: D

Categorical data one hot always

upvoted 1 times

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GiorgioGss

7 months 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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