Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
An engineer needs to identify the root cause of a job failure. Which THREE of the following are valid locations or methods to investigate the logs?
⚠ Common exam trap
Candidates often overlook 'Cluster event logs' as a source of information, focusing only on Spark logs, which misses infrastructure-level failures like spot instance terminations or node provisioning errors.
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 event logs
Troubleshooting in Databricks requires access to various log levels. The cluster event log captures infrastructure-level changes, driver logs provide application execution details, and executor logs offer task-specific debugging info. Using these three sources provides a holistic view, covering everything from cluster startup issues to specific code-level exceptions. This multi-layered approach is essential for isolating whether a problem is infrastructure-related, configuration-based, or rooted in the logic of the transformation code itself.
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 event logs
Why this is correct
Cluster event logs track infrastructure activities, such as node additions, removals, and configuration changes. These logs are essential for determining if a job failed due to node provisioning issues or cluster lifecycle events, which are distinct from code-level errors occurring during the Spark job execution.
- ✗
Notebook workspace browser history
Why it's wrong here
The browser history simply tracks user navigation within the Databricks UI. It does not store execution logs, error messages, or diagnostic data related to job failures. It is irrelevant to technical troubleshooting and does not contain any information about the performance or execution status of jobs.
- ✓
Spark Driver logs
Why this is correct
Driver logs contain the main application execution logs, including stack traces for exceptions, Spark context initialization errors, and scheduling information. This is usually the first place to look for identifying high-level application crashes or logical errors that stopped the job from completing its processing tasks.
- ✓
Spark Executor logs
Why this is correct
Executor logs contain logs from individual workers processing tasks. If a specific task fails due to data-related issues or memory errors, the executor logs provide the granular detail needed to identify exactly what happened on a specific node, which is crucial for troubleshooting distributed processing failures.
- ✗
Databricks account profile settings
Why it's wrong here
Account profile settings are for user preferences, security credentials, and identity management. They contain no information about job execution, cluster health, or error logs. Accessing these settings will not provide any diagnostic value when attempting to debug a failed Spark job in a production workspace.
About these practice questions
This Databricks-DE-Assoc question is part of Courseiva's 276-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-Assoc 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-Assoc exam.