Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer is troubleshooting a Databricks SQL query that occasionally fails with 'Query exceeded the maximum allowed execution time' on a shared SQL warehouse. The query is a complex aggregation over a large Delta table. The engineer needs to identify the root cause and ensure the query can complete successfully. Which action should the engineer take first?
⚠ Common exam trap
The trap here is jumping to scaling up the warehouse, which may mask the symptom but not fix the underlying inefficiency that the query profile would reveal.
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
✓
Examine the query profile in the Databricks SQL query history to identify stages with high data skew or spill.
The query profile in Databricks SQL provides detailed execution metrics that can reveal the root cause of long-running queries, such as data skew or spill. By analyzing the profile first, the engineer can make targeted optimizations, such as repartitioning or rewriting the query, which may resolve the timeout without unnecessarily increasing compute resources.
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 SQL warehouse size to provide more compute resources for the query.
Why it's wrong here
Increasing warehouse size may help with resource contention but does not address underlying inefficiencies such as data skew or inefficient joins. It may also increase cost. The engineer should first diagnose the root cause using the query profile before scaling up, as scaling may not resolve the issue if the query is inherently inefficient.
- ✗
Set the Spark configuration 'spark.sql.adaptive.enabled' to false to disable adaptive query execution.
Why it's wrong here
Disabling adaptive query execution would likely worsen performance, as AQE dynamically optimizes query plans based on runtime statistics. It is not a recommended troubleshooting step. The default is enabled, and turning it off could lead to less efficient execution plans and longer runtimes.
- ✓
Examine the query profile in the Databricks SQL query history to identify stages with high data skew or spill.
Why this is correct
The query profile provides detailed execution metrics, including time spent per stage, data skew, and spill to disk. These insights help pinpoint why the query exceeds the time limit, such as an inefficient join or skewed data distribution. Addressing these issues can allow the query to complete within the limit.
- ✗
Change the query to use a larger cluster by switching from a SQL warehouse to an all-purpose cluster.
Why it's wrong here
Switching to an all-purpose cluster changes the execution environment and may not be cost-effective or aligned with the SQL warehouse usage. It also does not guarantee a fix if the query itself is inefficient. The first step should be to analyze the query profile to understand the bottleneck, rather than changing the compute platform.
Visual reference
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.