Databricks Certified Data Engineer - Associate

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Explanation:

Explanation

The correct answer is D because the EXPLODE function is specifically designed to unnest array columns in Spark SQL.

Why EXPLODE is the correct choice:

EXPLODE takes an array column and creates a new row for each element in the array
For a row with cart_id = "cart1" and items = ["itemA", "itemB", "itemC"], EXPLODE(items) would generate:
- ("cart1", "itemA")
- ("cart1", "itemB")
- ("cart1", "itemC")
This perfectly transforms the nested array structure into the desired flat table format

Why other options are incorrect:

A. FILTER: Used to filter elements from an array based on a condition, not to unnest arrays
B. FLATTEN: Used to flatten nested arrays (arrays of arrays), not to explode single arrays into rows
C. REDUCE: Used for aggregating array elements using a function, not for unnesting
E. SLICE: Used to extract a subset of elements from an array, not to create new rows

Key Concept:

EXPLODE is the standard Spark SQL function for converting array elements into individual rows, which is exactly what's needed for this data transformation task.

Explanation:

Explanation

The correct answer is D because the EXPLODE function is specifically designed to unnest array columns in Spark SQL.

Why EXPLODE is the correct choice:

EXPLODE takes an array column and creates a new row for each element in the array
For a row with cart_id = "cart1" and items = ["itemA", "itemB", "itemC"], EXPLODE(items) would generate:
- ("cart1", "itemA")
- ("cart1", "itemB")
- ("cart1", "itemC")
This perfectly transforms the nested array structure into the desired flat table format

Why other options are incorrect:

A. FILTER: Used to filter elements from an array based on a condition, not to unnest arrays
B. FLATTEN: Used to flatten nested arrays (arrays of arrays), not to explode single arrays into rows
C. REDUCE: Used for aggregating array elements using a function, not for unnesting
E. SLICE: Used to extract a subset of elements from an array, not to create new rows

Key Concept:

EXPLODE is the standard Spark SQL function for converting array elements into individual rows, which is exactly what's needed for this data transformation task.

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Question 20

A junior data engineer has ingested a JSON file into a table raw_table with the following schema:

cart_id STRING,
items ARRAY<item_id:STRING>

cart_id STRING,
items ARRAY<item_id:STRING>

The junior data engineer would like to unnest the items column in raw_table to result in a new table with the following schema:

cart_id STRING,
item_id STRING

cart_id STRING,
item_id STRING

Which of the following commands should the junior data engineer run to complete this task?

Real Exam

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LLeetQuiz

SELECT cart_id, filter(items) AS item_id FROM raw_table;

3.6%

SELECT cart_id, flatten(items) AS item_id FROM raw_table;

10.2%

SELECT cart_id, reduce(items) AS item_id FROM raw_table;

2.2%

SELECT cart_id, explode(items) AS item_id FROM raw_table;

81.8%