Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
Which Databricks feature should a data engineer use to view the execution plan, including information about the physical operators and data lineage, to troubleshoot a slow-running SQL query?
⚠ Common exam trap
Candidates look at the general cluster metrics or job logs instead of navigating directly to the Spark UI SQL tab for physical execution plans.
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 Spark UI SQL tab.
The Spark UI (specifically the SQL tab) provides a comprehensive graphical and textual representation of the query execution plan. By examining the 'Analyzed Plan' and 'Physical Plan', engineers can identify bottlenecks like expensive joins, full table scans, or lack of pruning. Mastering these diagnostic tools is essential for optimizing query performance and ensuring that execution logic aligns with the intended data processing requirements in the Databricks environment.
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 Delta Lake History logs.
Why it's wrong here
Delta Lake history logs are used to audit table changes, view versioning information, and facilitate time travel. They contain metadata about transactions but do not provide the execution plans, physical operators, or performance metrics necessary to diagnose why a specific SQL query is running slowly or inefficiently.
- ✓
The Spark UI SQL tab.
Why this is correct
The Spark UI's SQL tab provides detailed information on how Spark parses, optimizes, and executes a query. It allows engineers to inspect the physical plan, identify time-consuming operators, and check for issues such as skewed joins or excessive data shuffling, making it the primary tool for query troubleshooting.
- ✗
The Cluster Metrics dashboard.
Why it's wrong here
Cluster metrics track hardware utilization such as CPU, memory, and network throughput over time. While useful for identifying resource starvation, these metrics do not show the specific SQL execution plan or the logic behind the data transformation, making them insufficient for debugging query-level performance problems or logical bottlenecks.
- ✗
The Data Explorer.
Why it's wrong here
The Data Explorer is designed for inspecting schema, table properties, and file-level metadata within the Databricks Unity Catalog or Hive Metastore. It serves as a visual interface for discovery and management but does not provide runtime performance data or query execution plans for active or historical SQL jobs.
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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-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.