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DP-203 Practice Question: Designing a batch processing pipeline that reads…
You are designing a batch processing pipeline that reads CSV files from Azure Blob Storage, performs aggregations using Azure Databricks, and writes results to Azure Synapse Analytics. The pipeline must handle schema drift (new columns appearing in source files). Which approach should you recommend?
⚠ Common exam trap
Test-takers frequently confuse schema drift handling with schema enforcement, assuming that a fixed sink schema or streaming pre-processing can accommodate dynamic schema changes, when in fact only a schema-on-read approach like Spark's `mergeSchema` with Delta Lake provides the necessary flexibility for batch pipelines.
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 Spark with mergeSchema option when reading, and write using a Delta table to evolve schema automatically.
Spark's `mergeSchema` option, when used with Delta Lake, automatically evolves the schema to accommodate new columns in CSV files. This allows the batch pipeline to handle schema drift without manual intervention, and writing to a Delta table ensures the schema evolution is persisted and compatible with downstream writes to Azure Synapse Analytics.
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 Azure Data Factory mapping data flows with schema drift enabled, mapping to a fixed sink schema.
Why it's wrong here
Sink schema would reject new columns.
- ✗
Define a fixed schema in the source and ignore any new columns.
Why it's wrong here
Ignores drift, losing data.
- ✓
Use Spark with mergeSchema option when reading, and write using a Delta table to evolve schema automatically.
Why this is correct
Handles schema drift automatically.
- ✗
Use Azure Stream Analytics to pre-process and enforce schema.
Why it's wrong here
Batch processing, not streaming.
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 |
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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