AI Associate Data for AI Practice Question
Which TWO data preparation steps are critical for ensuring high-quality training data?
⚠ Common exam trap
Salesforce often tests the distinction between data preparation steps that ensure data quality (like removing duplicates and handling missing values) versus optional preprocessing or augmentation techniques, leading candidates to mistakenly select normalization or noise addition as critical steps.
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
✓
Removing duplicate records.
Duplicate records in a dataset can cause the model to overfit to repeated patterns, biasing the learned distribution and reducing generalization. Removing duplicates ensures each data point contributes equally to training, which is essential for robust model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing dataset size by adding noise.
Why it's wrong here
Adding noise without care can degrade data quality.
- ✓
Removing duplicate records.
Why this is correct
Duplicates can overrepresent certain patterns and skew model training.
- ✗
Normalizing all features.
Why it's wrong here
Normalization is important for some models but not always critical for data quality.
- ✓
Handling missing values appropriately.
Why this is correct
Missing values can cause errors or bias if not properly addressed.
- ✗
Using only labeled data.
Why it's wrong here
This depends on the problem; unlabeled data can be useful for unsupervised learning or semi-supervised approaches.
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