DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is profiling a dataset and notices that the 'age' column contains negative values and values exceeding 120. The analyst needs to address these anomalies. Which data preparation technique is most appropriate?
⚠ Common exam trap
Many candidates confuse outlier treatment with imputation, which is for missing values, or normalization, which only rescales data without fixing errors.
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
✓
Outlier detection and treatment
The most appropriate technique is outlier detection and treatment because negative ages and ages over 120 are statistically implausible and likely errors. This method identifies and corrects or removes such values, ensuring the dataset's integrity. Other techniques like imputation or normalization do not address the underlying invalidity of the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data aggregation
Why it's wrong here
Data aggregation combines multiple records into summary statistics, such as averages. It does not correct individual invalid values; instead, it might mask them. Aggregation is used for analysis, not for cleaning anomalies at the record level. The invalid ages would still affect aggregates if not treated first.
- ✗
Imputation
Why it's wrong here
Imputation is used to fill missing values, not to correct invalid ones. While imputation could replace outliers with estimated values, it does not address the root issue of invalid data. The negative and excessive ages are not missing; they are erroneous and should be handled differently.
- ✗
Normalization
Why it's wrong here
Normalization scales numerical data to a standard range, such as 0 to 1. It does not correct invalid values; it would simply rescale them, preserving the anomalies. Normalization is useful for algorithms sensitive to scale, but it does not resolve data quality issues like impossible ages.
- ✓
Outlier detection and treatment
Why this is correct
Negative ages and ages above 120 are outliers that likely represent data entry errors. Outlier detection identifies such values, and treatment (e.g., removal, capping, or correction) addresses them. This technique is specifically designed to handle values that fall outside a plausible range, ensuring data quality.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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