ExamTopics Logo
Mail Usteam@examtopics.com
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 9 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 9
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

HOTSPOT -
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
• Feature splitting
• Logarithmic transformation
• One-hot encoding
• Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Show Suggested Answer Hide Answer
Suggested Answer:
by abrarjahin at Jan. 28, 2025, 4:52 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. You can switch to a simple comment. It is better to Upvote an existing comment if you don't have anything to add.
Submit Cancel
chris_spencer
1 week, 4 days ago
city = one-hot encoding; type_year = feature splitting; size of the building = standardized distribution
upvoted 1 times
...
Corry
3 weeks, 4 days ago
Since the question highlight "similarly sized homes", which means those numbers of build size won't show skewed distribution. Thus, Size of the building should be standard distribution.
upvoted 1 times
...
molerowan
1 month, 2 weeks ago
Size of building (Square feet or Square Meters) = Logarithmic transformation Explanation: Building size is a numerical feature that often shows a skewed distribution and can have a non-linear relationship with price. Logarithmic transformation is suitable because: It helps normalize skewed distributions It can help linearize the relationship between size and price It's particularly useful for features that follow exponential or multiplicative patterns Real estate data often shows log-normal distributions
upvoted 3 times
...
molerowan
1 month, 2 weeks ago
Type_year (type of home and year it was built) = Feature splitting Explanation: This feature contains two different pieces of information (type and year) combined in one column. Feature splitting is appropriate because: It separates the compound feature into its components The type can then be one-hot encoded The year can be treated as a numerical feature This separation allows the model to learn from each component independently
upvoted 2 times
...
molerowan
1 month, 2 weeks ago
City (Name) = One-hot encoding Explanation: City names are categorical variables that don't have any numerical relationship with each other. One-hot encoding is the best technique for this type of data because: It creates binary columns for each unique city It avoids introducing artificial ordering between cities It allows the model to treat each city as an independent feature
upvoted 3 times
...
djeong95
1 month, 2 weeks ago
city = one-hot encoding; type_year = feature splitting; size of the building = standardized distribution
upvoted 2 times
...
abrarjahin
1 month, 3 weeks ago
Size of the building is standard distribution
upvoted 2 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:
PL-600
Dubai, 1 minute ago