Databricks-DE-Pro Data Transformation, Cleansing, Quality Practice Question
You are building a pipeline and notice that the 'Gold' layer tables are experiencing significant write latency due to frequent small file commits. What is the most effective way to resolve this while maintaining ACID integrity?
⚠ Common exam trap
Candidates frequently suggest vacuuming or partitioning to fix small file issues, confusing storage cleanup and partitioning strategies with the actual file compaction performed by OPTIMIZE.
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
✓
Run the 'OPTIMIZE' command periodically on the Gold layer tables.
The 'OPTIMIZE' command with 'ZORDER' is the standard way to fix the small file problem in Delta Lake. It compacts small files into larger, better-performing ones while physically organizing data to speed up reads. This is critical for Gold tables, which are typically read by BI tools. Maintaining performance here is essential for providing end-users with a responsive, high-performance experience when accessing critical business metrics and dashboards.
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 'spark.sql.shuffle.partitions' to 5000 to distribute writes more widely.
Why it's wrong here
Increasing shuffle partitions doesn't fix small files; it often creates more of them by spreading data too thin across many partitions. This approach can actually lead to even more small files being created, worsening the latency issue instead of solving it for the target Delta table.
- ✓
Run the 'OPTIMIZE' command periodically on the Gold layer tables.
Why this is correct
The OPTIMIZE command is specifically built to compact small files in Delta tables. By running it on a schedule or as part of the pipeline, you ensure that Gold tables remain performant for readers. It is an essential maintenance task in any production-grade Delta Lake environment to ensure high-performance query execution.
- ✗
Switch the table from Delta format to standard Parquet files to improve write performance.
Why it's wrong here
Switching to raw Parquet files would lose the ACID capabilities of Delta Lake, such as time travel, schema enforcement, and reliable streaming. This is a massive regression in data quality and reliability, and it does not guarantee better performance for multi-user read/write scenarios in the same way that optimized Delta tables do.
- ✗
Use the 'vacuum' command every 5 minutes to clear out the small files.
Why it's wrong here
The VACUUM command is used to remove deleted data files that are no longer referenced by the transaction log, not to compact active data. Running it too frequently is a misuse of the feature and could lead to issues with time travel or long-running transactions that need access to older files.
Visual reference
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-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.