Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer is responsible for a production Databricks SQL warehouse that serves multiple teams. The engineer needs to set up monitoring to detect when query performance degrades due to resource contention. Which two metrics should the engineer monitor to identify this issue? (Choose two.)
⚠ Common exam trap
Candidates often confuse general workspace metrics, such as active clusters or storage usage, with SQL warehouse-specific performance indicators that actually reveal contention.
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 number of queries waiting in the queue for the warehouse.
Resource contention in a SQL warehouse manifests as queries waiting in the queue and increased query execution times. Monitoring queue length and average running time provides direct insight into whether the warehouse is oversubscribed. These metrics are available in the Databricks SQL warehouse monitoring dashboard and can be used to trigger scaling or alerting.
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 queries waiting in the queue for the warehouse.
Why this is correct
A growing queue indicates that queries are waiting for compute resources, which is a direct sign of resource contention. Monitoring the queue length helps identify when the warehouse is oversubscribed. This metric is available in the Databricks SQL warehouse monitoring dashboard and can be used to trigger alerts or scaling actions.
- ✗
The total storage used by the Unity Catalog metastore.
Why it's wrong here
Storage usage in the metastore does not affect query performance in a SQL warehouse. While storage capacity is important for data management, it is not a metric that indicates resource contention during query execution. Monitoring it would not address the engineer's goal.
- ✗
The number of active clusters in the workspace.
Why it's wrong here
The number of active clusters is unrelated to SQL warehouse performance. SQL warehouses are separate compute resources, and their performance is not directly affected by the number of all-purpose or job clusters in the workspace. Monitoring this metric would not help identify contention within the SQL warehouse.
- ✗
The number of failed login attempts to the workspace.
Why it's wrong here
Failed login attempts are a security metric, not a performance metric. They do not reflect query execution or resource contention in a SQL warehouse. Monitoring this would be relevant for security auditing but not for detecting performance degradation due to contention.
- ✓
The average time queries spend in the 'RUNNING' state.
Why this is correct
An increase in the average running time of queries can indicate that queries are competing for resources, leading to longer execution times. This metric, when combined with queue length, provides a clear picture of contention. It is available in the query history and warehouse monitoring metrics.
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.