DP-203 Develop data processing Practice Question
Your company uses Azure Data Lake Storage Gen2 and Azure Databricks for data processing. Some Parquet files in the lake are written with a schema that includes a column 'address' of struct type. A downstream process expects 'address' to be a string. You need to transform the data in a way that minimizes read overhead and does not rewrite the entire dataset. Which approach should you use?
⚠ Common exam trap
Candidates often confuse schema evolution (which modifies metadata but still requires a rewrite for type changes) with schema-on-read (which applies transformations at query time without altering storage), leading them to choose option A incorrectly.
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
✓
Apply a schema-on-read transformation that casts the column to string when reading.
Schema-on-read allows you to cast the 'address' column from struct to string at query time without modifying the underlying Parquet files. This approach minimizes read overhead by avoiding a full data rewrite and leverages Spark's ability to apply transformations during the read path, which is efficient for downstream processes that expect a string type.
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 schema evolution in Delta Lake to change the column type.
Why it's wrong here
Delta Lake schema evolution does not support changing column types from struct to string.
- ✓
Apply a schema-on-read transformation that casts the column to string when reading.
Why this is correct
Efficient, no rewrite needed.
- ✗
Create a view that selects the column as a string and save it as a new table.
Why it's wrong here
Would create a new copy of data.
- ✗
Rewrite the Parquet files with the correct schema using a Spark job.
Why it's wrong here
Unnecessary full rewrite.
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