Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer is configuring monitoring for a production Databricks cluster. Which TWO metrics are best suited to identify potential performance bottlenecks related to worker nodes?
⚠ Common exam trap
Candidates often pick storage or network metrics like disk IOPS or network throughput, forgetting that CPU and memory are the primary indicators of worker node compute 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
✓
Cluster CPU utilization percentage.
Monitoring worker nodes is vital for identifying bottlenecks before they cause job failures. Metrics like CPU utilization and memory usage are key indicators of node pressure. By identifying these patterns, engineers can optimize cluster configurations, adjust instance types, or tune spark code, ensuring that production workloads remain stable, performant, and cost-effective throughout their execution lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cluster CPU utilization percentage.
Why this is correct
High CPU utilization across worker nodes is a primary indicator of compute-bound tasks or inefficient code. Monitoring this helps identify when a cluster is saturated, potentially leading to slow query execution or timeouts. Regularly tracking this metric allows for proactive auto-scaling configurations to handle varying workload demands effectively.
- ✗
Number of active jobs currently running in the entire workspace.
Why it's wrong here
While useful for global workspace capacity, the total number of jobs does not reflect the performance of a specific cluster or its worker nodes. This is a global metric rather than a node-level performance indicator, making it ineffective for diagnosing bottlenecks within an individual compute resource or cluster.
- ✓
Cluster memory usage percentage.
Why this is correct
Memory usage is critical for identifying out-of-memory errors and performance degradation caused by excessive spilling to disk. If worker nodes consistently show high memory pressure, it suggests that the instance type is insufficient for the data volume, necessitating a switch to memory-optimized instances or data partition refactoring.
- ✗
Total number of users logged into the Databricks workspace.
Why it's wrong here
User login counts are unrelated to cluster performance. While this is a relevant metric for security monitoring or cost allocation, it provides zero insight into the computational efficiency or health of a Spark cluster. It should not be used as a primary metric for identifying data processing bottlenecks.
- ✗
The version of the Databricks Runtime used by the cluster.
Why it's wrong here
The runtime version is a configuration property, not a performance metric. While choosing the right runtime impacts performance, simply monitoring the version does not help in identifying real-time compute bottlenecks or node-level resource contention. Performance monitoring requires telemetry data that fluctuates based on current workload and data volume.
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.