DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset with missing values in a time series of daily temperatures. The missing values occur sporadically. Which imputation method is most appropriate to maintain the temporal trend?
⚠ Common exam trap
The trap is that mean and median imputation are the most commonly taught missing-value fixes, so candidates default to them without recognizing that time-ordered data requires methods that respect temporal continuity.
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
✓
Interpolation
Interpolation estimates missing values by using the values immediately before and after the gap, producing a smooth curve that preserves the temporal trend of a time series. For sporadically missing daily temperatures, linear interpolation between neighboring days is the most faithful reconstruction. It respects the ordered, continuous nature of the data better than simple fill methods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Forward-fill
Why it's wrong here
Forward-fill repeats the last observed temperature across each gap, creating artificial flat runs and lagging genuine trend changes, so it distorts rather than maintains temporal structure. It suits step-like or state data, not continuously varying measurements where interpolation between neighbours is needed.
- ✗
Mean imputation
Why it's wrong here
Mean imputation substitutes the dataset-wide average for every gap, pulling values toward the centre and damping the seasonal and directional movement the trend requires. It is used for independent observations with no ordering, not time series where adjacent points inform the missing value.
- ✗
Median imputation
Why it's wrong here
Median imputation replaces each gap with one global central value, flattening local variation and erasing the day-to-day temporal trend the analyst must preserve. It suits cross-sectional data with outliers, not ordered time series where neighbouring observations carry the signal.
- ✓
Interpolation
Why this is correct
Interpolation estimates missing points from neighbouring known values along the time axis, preserving the daily temperature trend and seasonality. Mean or median imputation flattens local variation, whereas interpolation maintains temporal continuity, satisfying the requirement to keep the series' trend intact.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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