Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer is building a Silver table in Delta Lake from a Bronze table that contains raw JSON events. The engineer needs to flatten a nested struct column named 'device' with fields 'type' and 'os', and also extract a field from an array of structs named 'events'. The goal is to produce a clean, denormalized Silver table. Which PySpark operation should the engineer use to achieve this transformation efficiently?
⚠ Common exam trap
Candidates often confuse explode() with other array functions like flatten(), or assuming that dot notation alone flattens nested structures.
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
✓
Use the select() method with 'device.type', 'device.os', and explode('events').alias('event') followed by selecting 'event.event_name'.
To flatten a struct, you select its fields directly. To flatten an array of structs, you explode the array to create multiple rows, then select the desired fields from the exploded struct. Combining these operations yields a denormalized table suitable for Silver. The other options either fail to flatten the struct, incorrectly use array functions, or do not fully denormalize the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the explode() function on the 'events' array and select 'device.type', 'device.os', and 'events.event_name'.
Why it's wrong here
Using explode() on the 'events' array will create a new row for each element, potentially duplicating the device information and increasing the row count. This is not ideal for denormalizing without careful aggregation, and it does not flatten the 'device' struct; it only accesses its fields via dot notation, which still leaves the struct intact. The goal is to flatten, not explode.
- ✗
Use the select() method with 'device.type', 'device.os', and 'events.event_name'.
Why it's wrong here
While select() with dot notation can access nested fields, it does not flatten the struct or array; it returns the nested structure as is. For a struct, the output column would still be a struct containing the selected fields, not separate top-level columns. For an array of structs, selecting 'events.event_name' would produce an array of event_name values, not individual columns. This does not achieve denormalization.
- ✗
Use the flatten() function on the 'events' array and select 'device.type', 'device.os', and 'events.event_name'.
Why it's wrong here
flatten() is used to merge nested arrays into a single array, not to extract fields from an array of structs. Applying flatten() to 'events' would not produce the desired scalar columns; it would combine arrays, which is not applicable here. This function does not address struct flattening either, so it fails to meet the requirement.
- ✓
Use the select() method with 'device.type', 'device.os', and explode('events').alias('event') followed by selecting 'event.event_name'.
Why this is correct
This approach flattens the 'device' struct by directly selecting its fields as separate columns, and uses explode() on the 'events' array to create a new row for each event, then selects the 'event_name' field from the exploded struct. This results in a denormalized table where each row represents an event with associated device information, which is a common pattern for Silver tables.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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