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DP-203 Handle schema evolution in Databricks Practice Question
When designing a data processing solution using Azure Databricks, what is the recommended approach to handle schema evolution when reading data from Delta Lake tables?
⚠ Common exam trap
Many candidates confuse 'mergeSchema' with 'overwriteSchema', mistakenly thinking both handle schema evolution similarly, but 'overwriteSchema' replaces the entire schema and can cause data loss, while 'mergeSchema' safely merges new columns or type changes.
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
✓
Set the option 'mergeSchema' to 'true' on write
In Delta Lake, schema evolution is automatically handled by setting the 'mergeSchema' option to 'true' on write operations. This allows new columns to be added or existing column types to be safely widened without manual intervention, preserving existing data and metadata integrity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the option 'overwriteSchema' to 'true' on write
Why it's wrong here
overwriteSchema replaces the entire schema and can cause data loss.
- ✗
Manually alter the table schema using ALTER TABLE
Why it's wrong here
Manual approach is error-prone and not recommended for automated pipelines.
- ✗
Ignore schema changes and use 'failOnDataLoss' flag
Why it's wrong here
failOnDataLoss is for streaming jobs, not for schema evolution.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-203 exam frequently reuses these exact scenarios with slightly different constraints.
✓Set the option 'mergeSchema' to 'true' on writeCorrect answer▾
✗Set the option 'overwriteSchema' to 'true' on writeWrong answer — click to see why▾
Why this is wrong here
overwriteSchema replaces the entire schema and can cause data loss.
✗Manually alter the table schema using ALTER TABLEWrong answer — click to see why▾
Why this is wrong here
Manual approach is error-prone and not recommended for automated pipelines.
✗Ignore schema changes and use 'failOnDataLoss' flagWrong answer — click to see why▾
Why this is wrong here
failOnDataLoss is for streaming jobs, not for schema evolution.
Analysis generated from the official DP-203blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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