DP-203 Develop data processing Practice Question
Network Topology
Refer to the exhibit. You submit a Spark job in Azure Synapse Analytics using the Azure CLI. The job runs slowly during the shuffle phase. The input data is about 200 GB. Which configuration change would best improve performance for this shuffle-heavy workload?
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
✓
Increase spark.sql.shuffle.partitions to 800.
Increasing spark.sql.shuffle.partitions to 800 improves parallelism for a 200 GB shuffle-heavy workload, reducing partition sizes and shuffle time. Option A is incorrect because executor count alone does not directly address shuffle partitioning. Option B may help but is less impactful than partition tuning. Option D would worsen the problem by making partitions larger.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of executors to 4.
Why it's wrong here
More executors help, but partition count is the primary issue for shuffle.
- ✗
Change executor size to 'Large' to increase memory per executor.
Why it's wrong here
Memory might help but shuffle performance is more affected by partition count.
- ✓
Increase spark.sql.shuffle.partitions to 800.
Why this is correct
More partitions reduce the size of each partition, speeding up shuffle.
- ✗
Decrease spark.sql.shuffle.partitions to 200 to reduce overhead.
Why it's wrong here
Decreasing partitions increases partition size, slowing shuffle.
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