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Amazon Exam MLS-C01 Topic 1 Question 88 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 88
Topic #: 1
[All MLS-C01 Questions]

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.

Which solution will meet this requirement with the LEAST development effort?

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Suggested Answer: A

SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.

References:

* Analyze and Visualize

* Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler


Contribute your Thoughts:

Kanisha
7 days ago
Whoa, hold up there, folks. Have you even considered option D? Multicollinearity feature, Lasso feature selection? That's where it's at! You get the importance scores and you get to do some sweet feature engineering. Efficiency at its finest, am I right?
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Nieves
8 days ago
Pfft, PCA? That's so yesterday. I'd vote for option C - singular value decomposition. It's the new hotness, trust me. Plus, you can get those importance scores without having to worry about all that pesky feature engineering. Just let the SVD work its magic!
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Derrick
9 days ago
I'm not so sure about that, my friend. What about PCA? We could use a SageMaker notebook instance and really dig into the data, you know? Find those hidden gems, the principal components that hold the real power. Sounds like a fun challenge to me!
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King
10 days ago
Hmm, this is a tricky one. I'd say option A is the way to go - SageMaker Data Wrangler makes it super easy to get those Gini importance scores, and it requires the least amount of work on our end. Plus, who doesn't love a good Gini index, am I right?
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