Databricks Certified Data Engineer - Associate
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In a data engineering project, you are tasked with categorizing individuals into specific age groups for a comprehensive analysis. The DataFrame 'df' contains columns 'id', 'name', and 'age'. The age groups are defined as follows: 'Child' (0-12 years), 'Teen' (13-19 years), 'Adult' (20-64 years), and 'Senior' (65+ years). The solution must accurately classify each individual into the correct age group and handle NULL values appropriately to avoid misclassification. Given these requirements, which of the following Spark SQL queries using the CASE/WHEN statement correctly assigns a new column 'age_group' based on the specified age ranges and ensures NULL values are handled correctly? Choose the two best options._
A
SELECT id, name, age, CASE WHEN age BETWEEN 0 AND 12 THEN 'Child' WHEN age BETWEEN 13 AND 19 THEN 'Teen' WHEN age >= 20 AND age <= 64 THEN 'Adult' ELSE 'Senior' END AS age_group FROM df_
B
SELECT id, name, age, CASE age WHEN age <= 12 THEN 'Child' WHEN age <= 19 THEN 'Teen' WHEN age <= 64 THEN 'Adult' WHEN age >= 65 THEN 'Senior' END AS age_group FROM df_
C
SELECT id, name, age, CASE WHEN age IN (0, 12) THEN 'Child' WHEN age IN (13, 19) THEN 'Teen' WHEN age IN (20, 64) THEN 'Adult' WHEN age >= 65 THEN 'Senior' END AS age_group FROM df_
D
SELECT id, name, age, CASE WHEN age < 13 THEN 'Child' WHEN age < 20 THEN 'Teen' WHEN age < 65 THEN 'Adult' WHEN age >= 65 THEN 'Senior' END AS age_group FROM df_
E
SELECT id, name, age, CASE WHEN age IS NULL THEN NULL WHEN age < 13 THEN 'Child' WHEN age < 20 THEN 'Teen' WHEN age < 65 THEN 'Adult' ELSE 'Senior' END AS age_group FROM df_