Databricks-DE-Assoc Data Transformation and Modeling Practice Question
Exhibit
spark.conf.set("spark.databricks.delta.optimizeWrite.enabled", "true")
spark.conf.set("spark.databricks.delta.autoCompact.enabled", "true")Refer to the exhibit. A data engineer is running these commands before performing heavy write operations into a Delta table. What is the primary benefit of enabling these configurations?
⚠ Common exam trap
Candidates often think these settings are for data compression or security, overlooking their primary purpose: addressing the 'small file problem' to improve metadata and scan performance.
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
✓
It mitigates the small file problem and improves read performance.
Enabling 'optimizeWrite' and 'autoCompact' is a common strategy to mitigate the 'small file problem' in Delta Lake. 'OptimizeWrite' rearranges data before writing to ensure consistent, larger file sizes, while 'autoCompact' merges small files post-write. Together, they ensure that the table remains highly performant for read operations by maintaining an optimal file layout, which is critical for reducing metadata overhead and maximizing query scan efficiency in large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It increases the number of concurrent write operations allowed on the table.
Why it's wrong here
These settings are related to file layout optimization, not concurrency management. Concurrent writes are handled by Delta's optimistic concurrency control mechanisms. While efficient file management indirectly helps performance, it does not increase the limit on how many simultaneous write operations can be performed on a single Delta table.
- ✓
It mitigates the small file problem and improves read performance.
Why this is correct
By optimizing the writing process and automatically compacting files into larger, more efficient sizes, these settings prevent the creation of many small files that degrade read performance. This results in a cleaner, more readable table structure that allows the Spark engine to scan data more efficiently during analytical queries.
- ✗
It forces the data to be written in Parquet format only.
Why it's wrong here
Delta Lake already uses Parquet as its underlying storage format. These configuration settings are specific optimizations for how files are organized and compacted within the Delta framework; they do not change the underlying data format, which is a structural feature inherent to how Delta Lake functions by default.
- ✗
It disables the need for periodic VACUUM maintenance.
Why it's wrong here
VACUUM is required to remove unreferenced files from storage for cost management and regulatory compliance. These configurations only manage the size and structure of active files; they do not delete old or orphaned data files, so periodic maintenance with VACUUM remains a necessary part of Delta Lake lifecycle management.
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JA
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-Assoc 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-Assoc exam.