DP-203 Develop data processing Practice Question
You are tuning a Spark job in Azure Synapse Analytics that processes large Parquet files. The job currently takes too long due to data skew. Which three actions can improve performance? (Choose three.)
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 bucketing on the join key when writing intermediate data.
Options A, B, and E are correct. Repartitioning redistributes data. Salting adds a random key to break skew. Bucketing pre-partitions data. Option C is wrong because coalesce reduces partitions but does not address skew. Option D is wrong because increasing executor memory may help but does not directly solve skew.
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 bucketing on the join key when writing intermediate data.
Why this is correct
Bucketing pre-partitions data to avoid shuffle and reduce skew.
- ✓
Add a salt key to the join column to distribute the load.
Why this is correct
Salting breaks skew by adding randomness.
- ✗
Use coalesce to reduce the number of partitions.
Why it's wrong here
Coalesce reduces partitions but does not address skew.
- ✗
Increase executor memory to handle larger partitions.
Why it's wrong here
Memory increase may help but is not a direct fix for skew.
- ✓
Repartition the data on the skewed column.
Why this is correct
Repartitioning can distribute data more evenly.
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