Databricks-DE-Pro Cost and Performance Optimization Practice Question
Which metric should a data engineer prioritize when investigating a slow-running query in the Databricks SQL query history?
⚠ Common exam trap
Test-takers often focus on cluster CPU utilization alone, ignoring the query history execution time breakdown which exposes the exact stage causing 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
✓
The total execution time and breakdown of the query stages.
The 'Total Time' metric is decomposed into wait time, compilation time, and execution time. Identifying the execution time bottleneck helps determine if the issue is compute-bound, I/O-bound, or due to slow data retrieval. This information is vital for deciding whether to optimize the query code, adjust partitioning, or scale the warehouse, ensuring that engineering efforts are targeted at the right performance issues to maximize impact.
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 number of users logged into the workspace at the time.
Why it's wrong here
While system load can affect performance, the number of users is a secondary metric that doesn't explain why a *specific* query is slow. Focusing on query-level metrics like execution duration provides a direct technical insight, whereas user count is too broad to be actionable for performance tuning.
- ✓
The total execution time and breakdown of the query stages.
Why this is correct
The execution time breakdown helps isolate which part of the query is causing the slowdown (e.g., scanning, shuffling, or joining). This visibility is crucial for diagnosing the root cause—whether it is an inefficient join, lack of partitioning, or data skew—and is the first step in any performance optimization process.
- ✗
The color of the query status light in the UI.
Why it's wrong here
The status light only indicates if the query finished successfully or failed. It provides no information about performance, runtime, or bottlenecks. Relying on such trivial indicators is not a valid approach for technical performance analysis or debugging query performance issues in a production environment.
- ✗
The name of the user who submitted the query.
Why it's wrong here
The identity of the user is irrelevant to the query performance. Identifying the query author might be useful for communication, but it does not provide any technical insight into why the query is slow, nor does it suggest any optimization strategies for the underlying data processing logic.
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-Pro 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-Pro exam.