Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer is designing an ETL pipeline processing high-frequency streaming data into Delta tables on Databricks. The pipeline experiences frequent small file creation and high metadata overhead, degrading query performance. Which optimization technique should the engineer implement to resolve this issue?
⚠ Common exam trap
Many candidates select periodic manual OPTIMIZE commands rather than automatic write-time solutions, missing the proactive nature required for high-frequency streaming workloads.
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
✓
Enable spark.databricks.delta.optimizeWrite.enabled and spark.databricks.delta.autoCompact.enabled.
Implementing automated compaction via optimized write and Auto Compact merges small files during writes, maintaining optimal file sizes around 128MB. This technique is critical for streaming workloads because frequent micro-batches naturally produce excessive tiny files that severely degrade both metadata listing times and subsequent read scan performance across production environments.
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 Delta table version retention period to keep historic snapshots longer.
Why it's wrong here
Adjusting the data retention period only affects how many historical versions are preserved for time travel and vacuum operations. It has no direct impact on file sizing issues, small file accumulation, or read query performance degradation caused by streaming micro-batches.
- ✗
Enable predictive optimization to automatically manage compaction and vacuum operations.
Why it's wrong here
Predictive optimization intelligently identifies and runs maintenance operations like compaction and vacuum, but it operates asynchronously. For streaming pipelines with high ingestion rates, synchronous controls like optimized writes and Auto Compact are required immediately upon file creation.
- ✓
Enable spark.databricks.delta.optimizeWrite.enabled and spark.databricks.delta.autoCompact.enabled.
Why this is correct
Enabling optimized writes and Auto Compact forces Spark to shuffle data to achieve well-sized files prior to writing and automatically triggers a compaction pass when small files are detected. This directly targets the root cause of metadata bottlenecks in high-frequency streaming architectures.
- ✗
Switch the table format from Delta to Apache Parquet to leverage native cloud storage indexing.
Why it's wrong here
Replacing Delta Lake with standard Parquet eliminates transactional guarantees, time travel, and native optimization features like Z-Ordering and OPTIMIZE. Standard Parquet does not solve the small file problem inherent to continuous streaming micro-batch data ingestion pipelines.
Visual reference
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.