BlackFriday 2024! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

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?

Show Suggested Answer Hide Answer
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

Limited Time Offer

25%

Off

Get Premium AIGP Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel
Val
2 months ago
Hmm, D is the answer, no doubt. I'd be more worried if the question asked about machine learning in a blender!
upvoted 0 times
Francoise
28 days ago
Absolutely, without proper feature engineering, the model may not perform well.
upvoted 0 times
...
Tegan
1 months ago
Yes, that's correct. It's an important step in the machine learning process.
upvoted 0 times
...
Sena
1 months ago
I agree, D is definitely the answer. Feature engineering is all about extracting attributes and variables from raw data.
upvoted 0 times
...
...
Karina
2 months ago
I think all the options are correct to some extent, as feature engineering involves various processes.
upvoted 0 times
...
Lenora
2 months ago
I'm gonna go with B. Creating a learning schema sounds like the way to go, if you ask me.
upvoted 0 times
...
Virgina
2 months ago
D, for sure! Extracting those juicy features is the key to a successful ML model.
upvoted 0 times
Donte
15 days ago
Absolutely, feature engineering is where the magic happens in ML.
upvoted 0 times
...
Gracia
23 days ago
I think D is the most crucial step in the machine learning process.
upvoted 0 times
...
Tina
1 months ago
Definitely, it's all about finding the most relevant variables to improve performance.
upvoted 0 times
...
Yolando
1 months ago
Yes, extracting attributes and variables from raw data is crucial for building a good model.
upvoted 0 times
...
Lucina
1 months ago
I agree, extracting the right features can make or break a model.
upvoted 0 times
...
Janet
2 months ago
D, for sure! Extracting those juicy features is the key to a successful ML model.
upvoted 0 times
...
...
Rhea
2 months ago
I believe it's also about converting raw data into clean data to improve model performance.
upvoted 0 times
...
Joaquin
3 months ago
I agree with Desirae, it's important to select the right features for the model.
upvoted 0 times
...
Desirae
3 months ago
I think feature engineering is about extracting attributes and variables from raw data.
upvoted 0 times
...

Save Cancel