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IAPP Exam AIGP Topic 2 Question 11 Discussion

Actual exam question for IAPP's AIGP exam
Question #: 11
Topic #: 2
[All AIGP Questions]

In the machine learning context, feature engineering is the process of?

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Suggested Answer: D

In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.


by Lorean at Aug 23, 2024, 09:01 PM

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Val
3 months ago
Hmm, D is the answer, no doubt. I'd be more worried if the question asked about machine learning in a blender!
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Francoise
2 months ago
Absolutely, without proper feature engineering, the model may not perform well.
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Tegan
2 months ago
Yes, that's correct. It's an important step in the machine learning process.
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Sena
2 months ago
I agree, D is definitely the answer. Feature engineering is all about extracting attributes and variables from raw data.
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Karina
3 months ago
I think all the options are correct to some extent, as feature engineering involves various processes.
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Lenora
3 months ago
I'm gonna go with B. Creating a learning schema sounds like the way to go, if you ask me.
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Virgina
3 months ago
D, for sure! Extracting those juicy features is the key to a successful ML model.
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Donte
2 months ago
Absolutely, feature engineering is where the magic happens in ML.
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Gracia
2 months ago
I think D is the most crucial step in the machine learning process.
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Tina
2 months ago
Definitely, it's all about finding the most relevant variables to improve performance.
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Yolando
2 months ago
Yes, extracting attributes and variables from raw data is crucial for building a good model.
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Lucina
3 months ago
I agree, extracting the right features can make or break a model.
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Janet
3 months ago
D, for sure! Extracting those juicy features is the key to a successful ML model.
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Rhea
3 months ago
I believe it's also about converting raw data into clean data to improve model performance.
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Joaquin
4 months ago
I agree with Desirae, it's important to select the right features for the model.
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Desirae
4 months ago
I think feature engineering is about extracting attributes and variables from raw data.
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