Practice Data Preprocessing And Feature Engineering (4.2.3) - Design Methodologies for AI Applications

Practise this section

8 questions

Flashcard drill
3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is data cleaning?
Show hintHide hint
Think about why clean data is critical.
Question 2.
Why do we normalize data?
Show hintHide hint
Consider how different units could affect learning.
3Question 3.
What is the primary purpose of data cleaning?
- To improve model accuracy
- To fix inconsistencies in data
- To generate new features
Show hintHide hint
Consider the initial step before any model training.
4Question 4.
True or False: Feature engineering is unnecessary if the dataset is large.
- True
- False
Show hintHide hint
Think about how features impact model performance.
Question 5.
Given a dataset with numerous missing values and outliers, outline a detailed plan to preprocess this data for training a machine learning model.
Show hintHide hint
Break it down into cleaning, engineering features, and then scaling.
Question 6.
How can poor feature engineering lead to the failure of an AI application? Provide an example.
Show hintHide hint
Consider how representation of data informs learning.

4 more questions in this section. Enroll free to practise them all.