AIF-C01 Fundamentals of AI and ML Practice Question
Which TWO of the following are best practices for preparing training data for a machine learning model?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that removing all outliers is always beneficial, when in fact domain knowledge is required to distinguish between noise and legitimate extreme values that may be critical for model accuracy.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Handle missing values by imputing or removing them.
Handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Handle missing values by imputing or removing them.
Why this is correct
Missing values can cause errors or bias; imputation or removal is a standard practice.
- ✓
Split the data into training, validation, and test sets.
Why this is correct
This allows model evaluation on unseen data and helps detect overfitting.
- ✗
Remove all outliers to improve model robustness.
Why it's wrong here
Removing all outliers may discard important information; domain knowledge should guide outlier handling.
- ✗
Use the entire dataset for training to maximize data usage.
Why it's wrong here
Using all data for training leaves no data for evaluation, risking overfitting and poor generalization.
- ✗
Avoid shuffling the data to preserve original order.
Why it's wrong here
Shuffling is important to avoid order bias, especially when using stochastic gradient descent.
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