Databricks-Spark-Assoc Using Spark SQL Practice Question
Which THREE of the following are valid ways to monitor or debug Spark SQL query performance in Databricks?
⚠ Common exam trap
Candidates often incorrectly include 'DESCRIBE HISTORY' as a performance debugging tool. While useful for auditing, it is not a primary tool for analyzing query execution plans or stage-level bottlenecks.
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
✓
Use the 'EXPLAIN' statement to inspect the physical plan of the query.
Monitoring tools like the Spark UI, Query Profile, and Explain plans are critical for developers to understand how their SQL code executes. They provide visibility into shuffle sizes, stage durations, and physical plan generation. Effectively using these tools is the difference between writing performant code and creating bottlenecks. They allow developers to identify skewed data, suboptimal joins, and unnecessary data scanning, which are common issues in large-scale Spark SQL workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the 'EXPLAIN' statement to inspect the physical plan of the query.
Why this is correct
The EXPLAIN command is essential for viewing the logical and physical plans generated by the Catalyst optimizer. It reveals how Spark intends to execute the query, showing joins, filtering, and scans, which helps developers identify if the plan is optimized or if performance is hindered by inefficient operations.
- ✓
Examine the Spark UI to review stage-level details and task metrics.
Why this is correct
The Spark UI provides granular details about stage durations, shuffle read/write, and executor memory usage. It is the go-to tool for identifying bottlenecks, such as data skew or slow tasks, by letting developers drill down into individual task attempts and identify why specific parts of the job are slow.
- ✗
Manually restart the cluster every time a query takes more than 10 seconds.
Why it's wrong here
Restarting a cluster is a destructive and time-consuming operation that does not address the underlying query inefficiency. It is not a debugging tool. Instead, developers should profile the code to understand the performance bottleneck and optimize the query itself, rather than trying to brute-force a solution through cluster restarts.
- ✓
Use the Databricks Query Profile to visualize the query execution tree.
Why this is correct
The Databricks Query Profile provides a visual interface for the query execution plan. It highlights the most expensive operations and provides insights into where the query is spending its time, which is invaluable for debugging performance issues without needing to interpret complex text-based explain plans manually.
- ✗
Increase the driver memory to the maximum available for all jobs.
Why it's wrong here
Increasing driver memory is a reactive measure for Out-Of-Memory errors on the driver, not a performance debugging tool. It does not provide insight into why a query is slow. Over-allocating memory can lead to resource waste and does not resolve issues related to inefficient SQL logic or data skew.
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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-Spark-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-Spark-Assoc exam.