Question 565 of 760
DP-203 Develop data processing Practice Question
You are designing a batch processing solution using Azure Databricks. The data source is a large Parquet dataset stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The processing requires joining two datasets: one with 10 billion rows and another with 1 million rows. The cluster uses Photon runtime. Which optimization should you apply to minimize shuffle?
⚠ Common exam trap
Many candidates assume increasing cluster size (Option B) is a universal performance fix, but the DP-203 exam specifically tests the understanding that shuffle reduction techniques like broadcast joins are more impactful than simply adding more nodes, especially when one dataset is small enough to fit in executor memory.
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
✓
Broadcast the smaller table (1 million rows) to all worker nodes.
Broadcasting the smaller table (1 million rows) to all worker nodes is the correct optimization because it eliminates the need for a full shuffle during the join. With Photon runtime, broadcast joins are highly efficient as they replicate the small table to each executor, allowing map-side joins that avoid costly data movement across the network. Given the 10:1 row ratio, the 1-million-row table is well within the default broadcast threshold (10 MB compressed, configurable via spark.sql.autoBroadcastJoinThreshold), making this the most effective shuffle-minimization technique.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Broadcast the smaller table (1 million rows) to all worker nodes.
Why this is correct
Broadcasting the smaller table avoids shuffling the large table, significantly reducing data movement.
- ✗
Increase the cluster size to reduce shuffle overhead.
Why it's wrong here
Increasing cluster size may improve parallelism but does not reduce the amount of data shuffled.
- ✗
Create bucketed tables on the join key for both datasets.
Why it's wrong here
Creating bucketed tables is a valuable optimisation for minimising shuffle in large-large joins, as it enables bucket-aware processing where only corresponding buckets are joined. However, for a join between a 10 billion-row and a 1 million-row dataset, Databricks' optimiser, particularly with Photon runtime, would typically employ a broadcast hash join. This strategy eliminates shuffle for the larger table entirely by sending the smaller dataset to all executors, which is a more effective shuffle reduction mechanism in this specific large-small scenario than bucketing both datasets.
- ✗
Use Delta Lake and optimize file layout with OPTIMIZE command.
Why it's wrong here
OPTIMIZE improves file layout but does not directly reduce shuffle during joins.
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Last reviewed: Jun 24, 2026
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