DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are optimizing a batch processing job in Azure Databricks that reads data from Azure Data Lake Storage Gen2 and writes aggregated results back. The job currently runs slowly due to high shuffle writes. You plan to use Delta Lake and optimize the table layout. Which two actions should you take to reduce shuffle writes? (Select two.)
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 most frequently used filter column.
To reduce shuffle writes in a Databricks batch job using Delta Lake, you should partition the Delta table by the most frequently used filter column (B) and use the OPTIMIZE command with Z-Ordering on join keys (E). Partitioning limits data shuffling by filtering out irrelevant partitions, and Z-Ordering colocates related data, reducing shuffle size during joins. Option A (auto-optimize) helps compaction but does not directly reduce shuffle writes. Option C (broadcast hash join) only helps if one table is small, not for large tables. Option D (increasing shuffle partitions) often increases shuffle writes due to more tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Delta Lake auto-optimize to coalesce small files.
Why it's wrong here
Auto-optimize helps with file size but does not directly reduce shuffle writes.
- ✓
Partition the Delta table by the most frequently used filter column.
Why this is correct
Partitioning reduces the amount of data shuffled during queries that filter on that column.
- ✗
Use a broadcast hash join hint for all joins.
Why it's wrong here
Broadcast hash join is only useful when one table is small enough to fit in memory; otherwise it can cause errors.
- ✗
Increase the number of shuffle partitions to 400.
Why it's wrong here
Increasing shuffle partitions can increase overhead and may not reduce shuffle writes.
- ✓
Use the OPTIMIZE command with Z-Ordering on join keys.
Why this is correct
Z-Ordering colocalizes related data, reducing data shuffling during joins and aggregations.
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