AI Associate Data for AI Practice Question
Exhibit
Total records: 10000 Missing values: 500 Duplicates: 200 Outliers in Amount: 50
Refer to the exhibit. A data analyst runs a profile on a dataset and sees these statistics. Based on best practices, which action should be taken first?
⚠ Common exam trap
Salesforce often tests the order of preprocessing steps, trapping candidates who jump to imputation or outlier removal without first cleaning duplicates, which is the foundational step in data preparation.
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
✓
Remove the 200 duplicate records
Duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute the 500 missing values with the mean
Why it's wrong here
Imputation is important but duplicates are a more obvious issue.
- ✓
Remove the 200 duplicate records
Why this is correct
Duplicates can artificially inflate certain patterns and cause data leakage.
- ✗
Remove the 50 outliers in the Amount field
Why it's wrong here
Outliers are not necessarily harmful and may contain signal.
- ✗
Skip all preprocessing and train the model directly
Why it's wrong here
Ignoring data quality degrades model performance.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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