- A
Use schema evolution in Delta Lake to change the column type.
Why wrong: Delta Lake schema evolution does not support changing column types from struct to string.
- B
Apply a schema-on-read transformation that casts the column to string when reading.
Efficient, no rewrite needed.
- C
Create a view that selects the column as a string and save it as a new table.
Why wrong: Would create a new copy of data.
- D
Rewrite the Parquet files with the correct schema using a Spark job.
Why wrong: Unnecessary full rewrite.
Question 141 of 851
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Schema-on-read in Spark uses the DataFrameReader's schema option or a select/cast operation to apply type conversions at read time, leveraging Parquet's columnar storage to read only the necessary columns. This avoids the cost of a full data shuffle or write, as the transformation is applied lazily during the execution plan. In real-world scenarios, this is critical for handling evolving schemas in data lakes where multiple consumers have different type expectations without duplicating storage.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-203 question test?
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply a schema-on-read transformation that casts the column to string when reading. — Option B is correct because 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.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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