ExamTopics Logo
Mail Us[email protected]
Menu
Amazon Discussions
exam questions

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

View all questions & answers for the AWS Certified Machine Learning Engineer - Associate MLA-C01 exam
Go to Exam

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 Hide Answer
Suggested Answer: D 🗳️
by GiorgioGss at Nov. 27, 2024, 9:19 p.m.
Disclaimers:
  • - ExamTopics website is not related to, affiliated with, endorsed or authorized by Amazon.
  • - Trademarks, certification & product names are used for reference only and belong to Amazon.

Comments

Chosen Answer:
This is a voting comment (?). It is better to Upvote an existing comment if you don't have anything to add.
Switch to a voting comment New
Submit Cancel
Saransundar
1 month 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 3 times
...
GiorgioGss
1 month, 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 1 times
...
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.

Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.

SaveCancel
exam
Someone Bought Contributor Access for:
SY0-701
London, 1 minute ago