Databricks-DA-Assoc Analyzing Queries Practice Question
A data analyst notices that a query involving a large join between two tables is consistently slow. The analyst suspects that one of the tables is significantly skewed. Which tool in the Databricks SQL query profile is most effective for confirming this skew?
⚠ Common exam trap
Candidates tend to look at cluster-level CPU utilization or overall duration metrics, missing task-level distribution details required to isolate specific data skew issues.
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 SQL Query Profile 'Metrics' tab.
The Query Profile provides visual metrics to identify bottlenecks. By examining the 'Max vs Median' row count metrics per task, an analyst can see if specific executors are processing significantly more data than others, indicating skew. Understanding data distribution is critical in Databricks, as skew often leads to 'straggler' tasks that delay entire query execution, regardless of overall cluster size or compute power.
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 Query History list view.
Why it's wrong here
The Query History list view provides high-level metadata such as duration, user, and status for historical queries. It does not contain granular task-level metrics or data distribution statistics necessary to diagnose skew issues within specific join operations or individual stages of the query execution plan.
- ✓
The SQL Query Profile 'Metrics' tab.
Why this is correct
The Query Profile metrics provide deep visibility into task-level statistics, including the minimum, maximum, and median rows processed per task. If the maximum row count significantly exceeds the median, it confirms data skew, allowing the analyst to implement partitioning strategies like salting to balance the workload across executors.
- ✗
The Cluster Usage dashboard.
Why it's wrong here
The Cluster Usage dashboard displays aggregate resource consumption metrics like DBU usage, CPU load, and memory utilization across a cluster. While useful for cost management and general capacity planning, it lacks the specific task-level row count details required to isolate data skew occurring within an individual query.
- ✗
The Table details metadata panel.
Why it's wrong here
The Table details panel provides information about table schema, file counts, and general statistics like table size. While it shows total row counts, it does not provide insight into how that data is distributed across partitions or how it is processed during complex join operations in real-time.
About these practice questions
Courseiva writes every Databricks-DA-Assoc question from scratch — 291 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-DA-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-DA-Assoc exam.