Databricks-DA-Assoc Analyzing Queries Practice Question
When analyzing query performance, what does a high 'Spill to Disk' metric indicate?
⚠ Common exam trap
Test-takers frequently attribute disk spilling to slow network latency or poor storage speeds rather than recognizing memory exhaustion.
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
✓
The cluster has insufficient memory for the operation.
Spilling to disk occurs when a query's operations, such as sorts or joins, exceed the available memory in the cluster's executors. This is a major performance bottleneck because disk I/O is exponentially slower than memory. Recognizing this indicator is crucial for analysts because it signals that the current cluster size or query logic needs adjustment to fit the workload comfortably within RAM, significantly improving query speed and stability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The query is successfully using the cache.
Why it's wrong here
Caching involves storing data in memory to avoid redundant reads. Spilling to disk is the opposite, indicating that there is insufficient memory. It is a sign of performance degradation, not an optimization, as the system is forced to swap data from memory to disk to complete the execution process.
- ✓
The cluster has insufficient memory for the operation.
Why this is correct
Spilling happens when a transformation, such as a large-scale shuffle or aggregate, consumes more memory than allocated. The system temporarily moves data to disk to prevent an 'OutOfMemory' crash. This slows the query down significantly, suggesting that the memory allocation per executor should be increased or the query optimized.
- ✗
The data is highly compressed.
Why it's wrong here
Compression affects storage size, but spilling is a runtime memory-management issue. High compression can actually reduce the likelihood of spilling by fitting more data into memory. Therefore, spilling is independent of compression levels and is specifically related to the relationship between the active working set and allocated RAM.
- ✗
The query is finished and writing results to the table.
Why it's wrong here
Writing results to a table is an 'Output' operation. Spilling is a background mechanism that happens during intermediate execution stages like joins or sorts. It is a symptom of memory pressure during calculation, not a part of the standard final write process to the target table or materialized view.
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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-DA-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-DA-Assoc exam.