Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer is troubleshooting a production Databricks job that intermittently fails with 'SparkOutOfMemoryError'. The job processes large datasets with skewed partitions. The engineer wants to monitor the job to proactively detect memory pressure before failures occur. Which metric should the engineer monitor on the driver and executor nodes?
⚠ Common exam trap
The trap here is focusing on symptoms like disk spilling or high CPU rather than the root cause—JVM heap memory usage—which directly signals memory pressure.
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
✓
JVM heap memory usage
JVM heap memory usage is the most direct indicator of memory pressure on Spark driver and executor nodes. By monitoring heap usage, engineers can detect when memory is approaching limits and intervene before OutOfMemoryError occurs. Other metrics like disk I/O, network throughput, and CPU utilization provide complementary information but do not directly measure memory consumption.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
JVM heap memory usage
Why this is correct
JVM heap memory usage on driver and executor nodes directly reflects memory pressure. When heap usage approaches the maximum, Spark may spill to disk or throw OutOfMemoryError. Monitoring heap usage allows the engineer to detect when memory is nearly exhausted and take action, such as increasing memory or repartitioning data, before job failure occurs.
- ✗
Disk I/O read/write throughput
Why it's wrong here
Disk I/O throughput indicates how much data is being read from or written to disk. While excessive disk I/O can be a symptom of memory spilling, it does not directly measure memory pressure. High disk I/O could also result from other factors like large shuffles. Therefore, it is not the primary metric for detecting imminent OutOfMemoryError.
- ✗
CPU utilization percentage
Why it's wrong here
CPU utilization measures how busy the processors are. High CPU usage might indicate compute-intensive tasks but does not reveal memory constraints. A job can have low CPU utilization while still suffering from memory pressure due to large data structures. Therefore, CPU utilization is not the right metric for proactive memory monitoring.
- ✗
Network throughput between executors
Why it's wrong here
Network throughput shows data transfer during shuffles or broadcasts. While high network usage may correlate with large data movement, it does not indicate memory exhaustion. Memory pressure can occur even with low network activity. Thus, network throughput is not a reliable indicator of impending OutOfMemoryError.
About these practice questions
Courseiva writes every Databricks-DE-Pro question from scratch — 267 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.