DA0-002 Data Concepts and Environments Practice Question
A data analyst is working with a dataset that contains a column for 'Order Date' stored as a string in the format 'YYYY-MM-DD'. The analyst needs to perform time-series analysis, such as calculating monthly sales trends. Which two actions should the analyst take to prepare the data for this analysis? (Choose two.)
⚠ Common exam trap
The trap here is assuming that dates can be normalized or averaged like numerical data, which is not appropriate for temporal analysis.
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
✓
Create a separate column for the month and year.
To perform time-series analysis on a date string, the analyst must first convert it to a proper date data type so that date functions can be applied. Additionally, creating separate month and year columns simplifies grouping and aggregation for monthly trends. These two steps ensure the data is in a usable format for temporal analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a separate column for the month and year.
Why this is correct
Creating separate columns for month and year facilitates grouping and aggregation for monthly trends. This derived column allows the analyst to easily group sales by month across years or by year-month combinations. It is a common data preparation step for time-series reporting when the tool does not support date functions directly.
- ✓
Convert the string to a date data type.
Why this is correct
Converting the string to a date data type allows the database or analysis tool to recognize it as a temporal value. This enables date functions like extracting month or year, and performing date arithmetic. Without conversion, the string would be treated as text, and time-series operations would not work correctly.
- ✗
Encode the date as a numeric timestamp.
Why it's wrong here
Encoding the date as a numeric timestamp (e.g., Unix time) is not necessary for time-series analysis and can make the data less interpretable. While timestamps are useful for precise time calculations, they are not required for monthly trend analysis and would add complexity without benefit.
- ✗
Normalize the date by subtracting the mean date.
Why it's wrong here
Normalizing a date by subtracting the mean date is not a standard practice for time-series analysis. Dates are not typically normalized in this way because it would destroy the temporal ordering and meaning. Time-series analysis relies on the actual dates to identify trends and seasonality, so this action is inappropriate.
- ✗
Replace missing dates with the average date.
Why it's wrong here
Replacing missing dates with an average date is invalid because dates are not numerical values that can be averaged meaningfully. Imputing missing dates with a central value would distort the time series and introduce bias. Missing dates should be handled by other methods, such as exclusion or interpolation based on time.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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