AI0-001 AI Models and Data Engineering Practice Question
A data scientist is cleaning a dataset. Which TWO actions are appropriate for handling missing data?
⚠ Common exam trap
CompTIA often tests the misconception that simple imputation methods like mean or mode are always safe, when in fact they can introduce bias and distort the dataset, making predictive imputation or deletion of rows with low missing rates more appropriate depending on the context.
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
✓
Use a predictive model to estimate missing values.
Using a predictive model to estimate missing values is a sophisticated imputation technique that leverages relationships between features to fill gaps, preserving data integrity and avoiding bias. This approach is particularly useful when data is not missing completely at random, as it can capture complex patterns that simpler methods miss.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore missing values and train the model directly.
Why it's wrong here
Most models cannot handle missing values directly.
- ✓
Use a predictive model to estimate missing values.
Why this is correct
Predictive imputation uses relationships in data, a valid advanced method.
- ✗
Impute missing values with the mean of the entire dataset.
Why it's wrong here
Mean imputation can distort distributions and is not always appropriate.
- ✓
Delete rows with missing values if the missing rate is low.
Why this is correct
If missing rate is low and data is MCAR, deletion is acceptable.
- ✗
Replace missing values with the most frequent value always.
Why it's wrong here
Replacing with mode is not universally appropriate and may introduce bias.
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