DP-203 Develop data processing Practice Question
You are building a data processing pipeline in Azure Synapse Analytics that ingests data from Azure Blob Storage and writes to a dedicated SQL pool. You need to ensure the pipeline can handle schema changes in the source data without failing. Which TWO actions should you take?
⚠ Common exam trap
It's easy for candidates to confuse PolyBase or staged copy with schema drift handling, but those features are designed for performance or staging, not for dynamic schema adaptation.
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
✓
Enable schema drift in a Mapping Data Flow activity.
Mapping Data Flows in Azure Synapse Analytics support schema drift, which allows the pipeline to dynamically handle changes in source data structure (e.g., new columns, changed data types) without failing. By enabling schema drift, the data flow can automatically detect and propagate these changes downstream, ensuring resilience against schema evolution.
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 staged copy with an intermediate storage.
Why it's wrong here
Staged copy improves performance but does not handle schema drift.
- ✓
Enable schema drift in a Mapping Data Flow activity.
Why this is correct
Mapping Data Flow can automatically handle schema drift by mapping new columns.
- ✗
Define a fixed schema in the source dataset.
Why it's wrong here
Fixed schema would cause errors when new columns appear in the source.
- ✗
Use PolyBase to load data into the dedicated SQL pool.
Why it's wrong here
PolyBase expects a fixed schema and will fail if the source schema changes.
- ✓
Use the Copy activity with AutoCreateTable enabled.
Why this is correct
AutoCreateTable automatically creates the sink table based on the source schema, adapting to changes.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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