Data Cleaning and Preparation | Dataford Interview Questions

Describe a situation where you had to clean and prepare data for analysis at YipitData. What challenges did you face?

For this exercise, use the provided observation table to return records suitable for analysis. Exclude incomplete, blank, or invalid observations while preserving valid zero values.

Output

One row per valid observation, with observation_id, cleaned source_name, cleaned metric_name, observed_value, and observed_on
Exclude rows with missing or blank source or metric names, missing values or dates, or negative values
Order by observed_on ascending, then observation_id ascending

Schema

yipitdata_observations

Column	Type	Description
observation_idPK	INT	Unique observation identifier
source_name	VARCHAR(100)	Name of the data source
metric_name	VARCHAR(100)	Name of the measured metric
observed_value	DECIMAL(12,2)	Recorded metric value
observed_on	DATE	Date associated with the observation

Your question is Data Cleaning and Preparation. Start with the requirements and the one table in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem