DP-203 Develop data processing Practice Question
You are designing a data processing solution using Azure Databricks. You need to read data from Azure Data Lake Storage Gen2, transform it using Spark SQL, and write to a Delta table. Which TWO configurations are required to ensure optimal performance for large datasets?
⚠ Common exam trap
Watch out — candidates often confuse auto-compaction as a Spark configuration (Option E) when it is actually a Delta Lake table property, and they overlook that caching (Option D) is not beneficial for write-heavy pipelines with large datasets.
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 Delta Lake's OPTIMIZE command to compact small files.
The OPTIMIZE command in Delta Lake compacts small files into larger ones, reducing the number of files that Spark must read during subsequent queries and writes. This is critical for large datasets where many small files can cause significant overhead in file listing and task scheduling. Option C is correct because Z-order optimization on frequently filtered columns improves data skipping, allowing Delta Lake to prune irrelevant files during scans, which dramatically reduces I/O and speeds up query performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable automatic schema detection to reduce overhead.
Why it's wrong here
Schema detection overhead is minimal and not a performance requirement.
- ✓
Use Delta Lake's OPTIMIZE command to compact small files.
Why this is correct
Compacting small files improves read performance.
- ✓
Use Delta Lake Z-order optimization on frequently filtered columns.
Why this is correct
Z-order improves data skipping.
- ✗
Cache the entire DataFrame in memory after reading.
Why it's wrong here
Caching might not fit large datasets and is not required.
- ✗
Enable auto-compaction in Spark configuration.
Why it's wrong here
Auto-compaction is a convenience, not a requirement.
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