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Amazon MLS-C01 Exam - 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?

Show Suggested Answer Hide Answer
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


by Wynell at Mar 15, 2024, 02:50 PM

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Georgiann
3 months ago
I disagree, lasso can be tricky with multicollinearity.
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Tegan
3 months ago
Wait, can lasso really give accurate importance scores?
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Felicia
3 months ago
Definitely go with Data Wrangler for less hassle!
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Sheron
4 months ago
I think PCA is more complex than needed here.
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Chau
4 months ago
Gini importance is super useful for feature selection!
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Serina
4 months ago
I feel like Gini importance from option A is straightforward and requires less coding effort compared to the other options.
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Lawrence
4 months ago
I practiced something similar where we used lasso for feature selection, but I can't recall if it directly gives importance scores. Option D seems a bit complex for this task.
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Leeann
4 months ago
I'm not entirely sure, but I remember PCA is more about dimensionality reduction rather than feature importance. So, option B might not be right.
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Barbra
5 months ago
I think option A might be the best choice since Data Wrangler is designed for quick data analysis and feature engineering.
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Ines
5 months ago
I'm a bit confused by the options here. Lasso feature selection for importance scores? That doesn't seem like the most direct approach. I think I'll go with option A or B - those seem like the more standard ways to approach this problem.
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Avery
5 months ago
Okay, I see what they're asking for. The key is to find the solution that requires the least development effort. Based on that, I think option A with the Gini importance score is probably the way to go. It's a built-in feature in SageMaker Data Wrangler, so it should be pretty straightforward to implement.
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Yvette
5 months ago
Hmm, I'm a bit unsure about this one. The question mentions feature engineering, so I'm wondering if PCA or SVD might be better options to uncover the underlying feature relationships. I'll have to think this through a bit more.
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Alaine
5 months ago
This looks like a straightforward feature importance analysis problem. I think option A using SageMaker Data Wrangler would be the easiest approach with the least development effort.
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Sanjuana
5 months ago
I'm a bit confused by the wording of the question. Let me re-read it carefully and make sure I understand what I'm supposed to do. I don't want to rush into this and get it wrong.
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Dominga
5 months ago
This looks like a tricky one. I'll need to carefully analyze the code and the options to determine which one can replace the if block.
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Minna
2 years ago
I think using multicollinearity feature for lasso selection would also be a good option for achieving importance scores.
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Ardella
2 years ago
PCA might be simpler, but Gini importance scores provide more accurate insights for feature selection.
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Jesusita
2 years ago
But wouldn't principal component analysis (PCA) be a simpler solution for obtaining feature importance scores?
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Johnson
2 years ago
I agree with Ardella, Gini importance score analysis is efficient for feature engineering.
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Ardella
2 years ago
I think the best solution would be to use SageMaker Data Wrangler for Gini importance score analysis.
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Dortha
2 years ago
I'm not sure, but I think option B) using SageMaker notebook for principal component analysis could also be a good approach.
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Angelyn
2 years ago
I disagree, I believe option D) using multicollinearity feature for lasso feature selection is more efficient.
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Corinne
2 years ago
I think option A) using SageMaker Data Wrangler for Gini importance score analysis is the best choice.
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Kanisha
2 years 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
2 years 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
2 years 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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Jodi
2 years ago
Sounds like we're all on board with PCA. Let's dig deep into those principal components!
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Nickolas
2 years ago
B) Use a SageMaker notebook instance to perform principal component analysis (PCA).
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Glendora
2 years ago
That's a good point. Lasso feature selection could also be a powerful technique for obtaining importance scores.
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Laura
2 years ago
D) Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
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Cordelia
2 years ago
Hmm, interesting idea. Gini importance score analysis could also provide valuable insights.
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Marshall
2 years ago
A) Use SageMaker Data Wrangler to perform a Gini importance score analysis.
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Alva
2 years ago
B) Use a SageMaker notebook instance to perform principal component analysis (PCA).
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King
2 years 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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