DP-203 Develop data processing Practice Question
You are designing a batch processing solution in Azure Databricks that reads Parquet files from Azure Data Lake Storage Gen2, performs aggregations, and writes results to a Delta table. The data volume is expected to grow to 10 TB per batch. You need to minimize shuffle operations during the aggregation step. Which approach should you recommend?
⚠ Common exam trap
It's easy for candidates to confuse partitioning (which co-locates data by key) with bucketing or coalesce, thinking any partition reduction will minimize shuffle, but only alignment of read and aggregation keys avoids the shuffle entirely.
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
✓
Partition the Delta table by the aggregation key and use the same partitioning when reading.
Partitioning the Delta table by the aggregation key and aligning the read partitioning ensures that data with the same key value is co-located in the same Spark partition. This allows the aggregation to be performed within each partition without needing to shuffle data across the network, which is the primary cause of performance bottlenecks in large-scale batch processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the Delta table by the aggregation key and use the same partitioning when reading.
Why this is correct
Partitioning on the aggregation key ensures data is already grouped, minimizing shuffle.
- ✗
Use bucketing on a different column to reduce partition skew.
Why it's wrong here
Bucketing on a different column does not help avoid shuffle for the aggregation key.
- ✗
Use a broadcast join to avoid shuffle on the aggregation.
Why it's wrong here
Broadcast join is for joins, not for aggregations.
- ✗
Use coalesce to reduce the number of partitions before aggregation.
Why it's wrong here
Coalesce reduces partitions but does not avoid shuffle; it may even increase it.
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