Databricks-DE-Assoc Data Transformation and Modeling Practice Question
Exhibit
spark.sql.streaming.checkpointLocation: /mnt/delta/checkpoints/sales_data spark.databricks.delta.optimizeWrite.enabled: true spark.databricks.delta.autoCompact.enabled: true
Refer to the exhibit. A data engineer is configuring a streaming pipeline. Which outcome will these specific configurations have on the target table's performance?
⚠ Common exam trap
Candidates often assume auto-compaction and optimized writes happen automatically on all tables, forgetting they must be explicitly configured or enabled in streaming scenarios.
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 will prevent the creation of small files, resulting in faster downstream read performance.
These settings are crucial for maintaining the performance of Delta tables that receive constant small writes. Auto-compacting and optimized writes prevent the 'small file problem,' where thousands of tiny files degrade read performance. By configuring these in a streaming context, the engineer ensures the table remains query-efficient over time, which is essential for BI users who require fast dashboard load times from the target Delta tables.
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 will increase the latency of the streaming write significantly.
Why it's wrong here
While auto-compaction adds a small amount of overhead to each write, the overall performance gain for read queries is significant. It does not significantly hinder the streaming write latency, as the compaction happens in the background. The trade-off is well worth the improved query performance for downstream consumers.
- ✓
It will prevent the creation of small files, resulting in faster downstream read performance.
Why this is correct
Optimized writes and auto-compaction work together to ensure that small files produced by streaming writes are merged into larger, more efficient files. This reduces metadata overhead and improves read performance for downstream users, ensuring the table remains performant and ready for analytics without needing frequent manual vacuuming or compaction.
- ✗
It will reduce the storage cost by automatically deleting old files.
Why it's wrong here
Auto-compaction and optimized writes do not delete old data; they reorganize it. Reducing storage costs is the responsibility of the 'VACUUM' command. Confusing file management with storage retention can lead to dangerous misconceptions about how data is lifecycle-managed within the Delta Lake table structure.
- ✗
It will force the pipeline to process data in batches rather than continuously.
Why it's wrong here
These settings are compatible with both trigger-once and continuous streaming modes. They do not dictate the frequency of processing; rather, they influence how the data is physically organized on disk after it has been ingested. They are purely performance optimization settings for the underlying storage layer.
About these practice questions
One of 276 original Databricks-DE-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-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.