Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer is investigating why a Databricks job that reads from a Delta table is slow. The job performs a simple SELECT with a filter on a partition column. The engineer suspects that the table has many small files. Which Spark UI tab should be examined to confirm the number of files read?
⚠ Common exam trap
The trap here is assuming the SQL tab is always the best place for query diagnostics, when for file-level metrics the Stages tab gives clearer input and task counts.
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
✓
Stages tab
The Stages tab provides per-stage metrics including the number of tasks, input size, and records read. For a scan stage, the number of tasks is often proportional to the number of files or partitions. A high task count with small input size per task suggests many small files. This makes the Stages tab the most direct place to confirm the small-file issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SQL tab
Why it's wrong here
The SQL tab shows query execution plans and metrics for SQL queries, including the number of files read and bytes scanned in the details of the scan node. However, for a DataFrame job, the SQL tab may not always show the physical plan details as clearly as the Stages tab. The Stages tab provides input size and records read per stage, which directly reflects the number of files.
- ✓
Stages tab
Why this is correct
The Stages tab in the Spark UI shows the number of tasks, input size, and records read for each stage. For a scan operation, the number of tasks often corresponds to the number of files or partitions read. By examining the input size and task count, the engineer can confirm whether many small files are being read, which indicates the small-file problem.
- ✗
Environment tab
Why it's wrong here
The Environment tab lists the Spark configuration properties and JVM settings for the application. It does not contain runtime metrics about file reads or task execution. While useful for verifying settings, it cannot confirm the number of files being read, so it is not the right place to investigate the small-file problem.
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Storage tab
Why it's wrong here
The Storage tab displays information about persisted RDDs and DataFrames, such as cached partitions and memory usage. It does not show the number of files read during a scan. Since the job does not cache the DataFrame, this tab would not provide relevant information about the small-file issue.
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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.