Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer notices that a production Delta Lake table is experiencing slow read performance during concurrent write operations. The table contains millions of small files. Which action should the engineer take to resolve this performance degradation?
⚠ Common exam trap
Candidates often suggest 'dropping and recreating the table' or 'increasing the cluster size', which are destructive or costly workarounds for a simple file management issue that OPTIMIZE is designed to solve.
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
✓
Execute the OPTIMIZE command on the table.
Compacting small files into larger files using the OPTIMIZE command reduces metadata overhead and improves I/O efficiency. This is a critical maintenance task in Databricks because small files cause excessive object storage requests and metadata listing latency. By consolidating these files, the engine can scan data much more effectively during read operations, even while concurrent writes are occurring, as Delta Lake ensures ACID transactions remain consistent and performant.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the VACUUM command on the table.
Why it's wrong here
VACUUM removes old data files that are no longer referenced by the Delta log beyond the retention period. While it helps with storage costs and cleanup, it does not address the performance impact caused by the presence of numerous small files during active read operations.
- ✗
Increase the number of worker nodes in the cluster.
Why it's wrong here
Increasing cluster size provides more computational resources but does not solve the fundamental bottleneck caused by small file metadata overhead. Without file compaction, the executor still spends excessive time opening many small files, leading to inefficient resource utilization regardless of total cluster capacity.
- ✓
Execute the OPTIMIZE command on the table.
Why this is correct
OPTIMIZE compacts small files into larger, optimally sized files, which significantly improves read performance. This process is essential for maintaining high-performance Delta tables that experience frequent streaming writes or high-frequency batch inserts, as it reduces the number of files the query engine must track and scan.
- ✗
Enable Z-Ordering on all columns in the table.
Why it's wrong here
Z-Ordering is a technique to colocate related information in the same set of files, which improves performance for filter-heavy queries. However, applying it to all columns is computationally expensive and does not directly resolve the performance overhead caused by the sheer number of small files.
Visual reference
About these practice questions
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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.