AI Associate Data for AI Practice Question
A machine learning team is preparing a dataset for a supervised learning task. They have 100,000 labeled samples. Which data preparation step is essential before splitting into train/test sets?
⚠ Common exam trap
Salesforce often tests the misconception that normalization or outlier removal must be done before splitting, but the trap here is that candidates overlook the fundamental need to randomize the data order to avoid temporal or structural bias in the train/test split.
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
✓
Shuffle the dataset randomly.
Shuffling the dataset randomly before splitting into train/test sets ensures that the data distribution is similar across both subsets. Without shuffling, the split might inadvertently separate ordered or grouped data (e.g., time-series or batches), leading to biased model evaluation. This step is essential for supervised learning to prevent data leakage and ensure the test set is representative of the overall population.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize all features to the same scale.
Why it's wrong here
Normalization should be fitted on training data only.
- ✗
Remove all outliers from the dataset.
Why it's wrong here
Removing outliers may bias the model and should be done carefully.
- ✓
Shuffle the dataset randomly.
Why this is correct
Shuffling prevents biased splits.
- ✗
Visualize the data distribution for each feature.
Why it's wrong here
Visualization is exploratory, not essential before split.
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