Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
Which action is recommended to resolve a scenario where a Databricks Job is failing due to excessive metadata operations on a Delta table with millions of files?
⚠ Common exam trap
Candidates often suggest partitioning the table as a fix. While partitioning helps with data skipping, it does not fix the metadata overhead caused by having millions of small files.
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 OPTIMIZE to consolidate files.
When a Delta table contains millions of small files, the transaction log and metadata operations become a bottleneck. The 'list' operations required to build the state of the table consume significant time and driver memory. Implementing partition pruning or using Delta Lake's table property 'delta.enableChangeDataFeed' are not the primary solutions here. Instead, running OPTIMIZE to consolidate files is the standard way to reduce metadata overhead and improve table performance.
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 driver node instance size.
Why it's wrong here
While a larger driver node might provide more memory to handle the large file list, this is a 'band-aid' solution. It does not address the underlying issue of excessive metadata operations. The root cause is the sheer number of files, which must be reduced to maintain a healthy table state.
- ✓
Run OPTIMIZE to consolidate files.
Why this is correct
Running OPTIMIZE reduces the number of files by merging small files into larger ones. This directly reduces the number of entries in the Delta log and the number of metadata calls required to resolve the table state, significantly improving the performance of subsequent queries and avoiding the metadata bottleneck.
- ✗
Disable the Delta transaction log.
Why it's wrong here
The Delta transaction log is the core component that provides ACID guarantees and snapshot isolation in Delta Lake. Disabling it is impossible and would result in a loss of the very features that make Delta tables reliable. The metadata overhead is a trade-off for these critical consistency features.
- ✗
Add more worker nodes to the cluster.
Why it's wrong here
Adding worker nodes increases the compute capacity for processing data, but it does not improve the performance of metadata operations which occur primarily on the driver node. The bottleneck is the interaction between the driver and the storage layer, which is not resolved by adding more parallel processing workers.
Visual reference
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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.