Databricks-DA-Assoc Creating Dashboards and Visualizations Practice Question
An analyst has a Databricks SQL dashboard with a table visualization showing the top 20 customers by lifetime value. The stakeholder wants the same numbers represented as proportional horizontal bars so differences between customers are easier to compare visually. Which visualization type should the analyst choose?
⚠ Common exam trap
The trap here is reaching for a pie chart because the request mentions proportions, when the real task is comparing many discrete categories by magnitude, which bars handle far better.
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
✓
Bar chart
A bar chart is the standard choice for comparing a numeric measure across discrete categories. Each customer gets a bar whose length equals lifetime value, so ranking and relative differences are immediately visible, and a horizontal orientation keeps long customer names legible. Databricks SQL's visualization editor supports horizontal bar charts and sorting, making it straightforward to present the top 20 customers in descending order.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Line chart
Why it's wrong here
A line chart implies a continuous progression, typically over time, and connects points with segments that suggest an ordered sequence. Customers are discrete categories with no inherent order, so connecting their lifetime values with a line would be misleading. It also becomes cluttered with 20 distinct categories and does not emphasize proportional magnitude comparison the way bars do.
- ✗
Pie chart
Why it's wrong here
A pie chart encodes each customer as a slice of a whole, which works poorly when there are many categories and when the values are not parts of a single total. With 20 customers, the slices become too thin to label, and comparing similar slice angles is harder than comparing bar lengths. It also does not naturally show ranking along a shared axis, which is what this stakeholder needs.
- ✗
Scatter plot
Why it's wrong here
A scatter plot is designed to show the relationship between two numeric measures, plotting points on x and y axes. Here there is only one measure, lifetime value, against a categorical dimension, so a scatter plot would waste an axis and obscure the ranking. It cannot directly represent the proportional magnitude of a single value per customer in the readable way a bar chart does.
- ✓
Bar chart
Why this is correct
A bar chart places each customer on a categorical axis and encodes lifetime value as bar length, which makes ranking and magnitude comparisons immediate. When oriented horizontally, long customer names remain readable on the y-axis. Databricks SQL visualizations support horizontal bar charts directly, and the built-in sorting lets the analyst order customers by value so the top 20 are clearly ranked.
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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.