Databricks-DA-Assoc Creating Dashboards and Visualizations — Timed 20 Questions

Question 1

An analyst wants to display aggregated revenue trends over time using Databricks Dashboards, ensuring the query executes efficiently and filters dynamically based on user selections. Which approach should the analyst implement?

20 minute time limit — choose an answer to begin.

Accepted Answer

Write a standard SQL query grouping by date and configure dashboard-level filter widgets linked to query parameters to drive dynamic data retrieval.. Databricks Dashboards rely on parameterization within SQL queries to pass user filter selections directly to the underlying data source. Implementing parameterized queries reduces compute overhead by filtering data at the source rather than pulling full datasets into the dashboard client, ensuring optimal execution performance and accurate time-series aggregation.

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Hardcode the date ranges directly inside the visualization editor to prevent users from accidentally querying large partitions of the underlying sales table.

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Export the raw revenue dataset into local browser memory and build JavaScript-based time-series filters within a custom HTML visualization card.

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Create separate static dashboards for every possible date combination required by different department stakeholders across the organization.

Question 2

A data analyst is building a dashboard and needs to ensure that end-users can refresh the data themselves without needing write access to the underlying SQL warehouse. Which feature should the analyst enable?

Accepted Answer

Enable the 'Run as viewer' credential setting.. Enabling 'Run as owner' allows dashboard end-users to view and refresh data using the credentials of the dashboard owner. This enables users who do not have direct access to the underlying tables or warehouse to still interact with and refresh the dashboard.

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Grant the user 'Can Edit' permissions on the dashboard.

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Enable the 'Run as owner' credential setting.

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Share the dashboard using a personal access token.

Question 3

Refer to the exhibit. An analyst is configuring dashboard filters. Based on the provided configuration, what is the expected behavior when a user changes the 'date_filter'?

Accepted Answer

The filter will propagate the selected date to all dashboard widgets.. The explanation claims the filter applies to all widgets automatically because it is a 'global_parameter', but the exam trap and common trap note correctly state that filters must be explicitly mapped to parameters in each widget. The explanation should reflect that the filter propagates only to widgets where the parameter is correctly mapped.

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Only the queries based on 'query_result' will be affected.

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The filter will only update the visualization that created the filter.

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The filter will cause an error because the source is not defined.

Question 4

An analyst wants to ensure that a dashboard is accessible to users with different screen sizes. What should the analyst consider regarding layout?

Accepted Answer

Use the built-in grid layout for automatic responsiveness.. In Databricks SQL dashboards, the layout is grid-based. This allows widgets to automatically reflow and resize based on the screen width, providing a responsive experience without requiring manual duplication of dashboards or hard-coding pixel values.

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Hard-code the width and height of all widgets in pixels.

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Place all visualizations in a single vertical column.

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Create separate dashboards for desktop and mobile users.

Question 5

An analyst is configuring permissions for a new executive dashboard. Which THREE of the following statements correctly describe the behavior of dashboard permissions in Databricks? (Choose three)

Accepted Answer

The owner of the dashboard can share the dashboard with other users and grant them 'Can Edit' permissions.. Option E is incorrect because dashboard visibility is controlled by the dashboard's ACLs, not by the Workspace Admin role. Any user granted 'Can View' access can see the dashboard. Option A is incorrect because 'Can View' does not allow modification. Option D is incorrect because dashboard permissions are independent of table permissions in Unity Catalog.

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Users with 'Can View' access can modify the underlying SQL queries of the dashboard widgets.

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Dashboards automatically inherit all permissions from the underlying Delta tables in the Unity Catalog.

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Only users with the Workspace Admin role can view dashboards created by other users.

Question 6

An analyst has built a Databricks SQL dashboard showing monthly revenue by region. A stakeholder wants to click on a specific region in a bar chart and have a second chart of that region's product mix update automatically. The analyst has already created both queries and added them to the dashboard. What should the analyst do to enable this behavior?

Accepted Answer

Ensure both visualizations are built from datasets that share the same region field, then use the dashboard's cross-filtering capability so that clicking a region in the bar chart filters the product-mix visualization.. Cross-filtering in Databricks SQL dashboards works when visualizations share a common field from their datasets. Once the region field is present in both the bar chart and the product-mix chart, enabling cross-filtering lets a click on a region bar drive a filter that refreshes the other visualization. This provides the interactive, selection-based behavior without requiring a parameter widget or manual intervention.

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On the dashboard, use the visualization's menu to add a dashboard-level filter for the region field, then set the product-mix visualization to listen to that filter so it refreshes when the region changes.

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On the bar chart, open the visualization settings and enable the option that lets selections in this chart filter other widgets, then configure the product-mix query to use the region field as a dashboard parameter.

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Configure the product-mix query to include a parameter, then add a parameter widget to the dashboard and manually change its value whenever a region is selected.

Question 7

An analyst has a Databricks SQL dashboard with a bar chart showing sales by product category. They want to add a second visualization that shows the same categories as a percentage of total sales, using the same underlying query result, without writing a new query. What is the most appropriate approach?

Accepted Answer

Duplicate the existing visualization and change its chart type to pie, which will automatically display each category as a percentage of the total.. Duplicating the visualization and switching it to a pie chart reuses the same query result while presenting each category as a share of the total, which is exactly what a parts-of-a-whole visualization does. Writing a new query is disallowed by the scenario, reformatting a bar chart does not recompute percentages, and multiple counters do not form a single percentage breakdown.

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Create a new query that computes percentages with a window function, then add it as a separate visualization.

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Change the existing bar chart's Y-axis to a percentage scale, which converts the bars into percentage-of-total values automatically.

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Add a counter visualization for each category, since counters display a single value that can be formatted as a percentage.

Question 8

Which visualization type is most appropriate for displaying the distribution of a single continuous numerical variable, such as transaction amounts, in a Databricks Dashboard?

Accepted Answer

Histogram. Histograms are specifically designed to display the frequency distribution of continuous numerical variables by grouping data into contiguous bins. Choosing the correct visualization ensures that stakeholders can instantly identify data skewness, central tendencies, and outliers without misinterpreting the underlying statistical distribution.

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Pie chart

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Line plot

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Scatter plot

Question 9

An analyst is creating a visualization of daily sales trends over the past year. They notice that the chart is cluttered and difficult to interpret due to the high volume of individual data points. Which action should the analyst take to improve readability?

Accepted Answer

Change the X-axis grouping from 'Day' to 'Month'.. Aggregating data by a larger time grain, such as weeks or months, is a best practice when visualizing high-frequency time-series data. This reduces noise and highlights long-term trends rather than daily fluctuations. By adjusting the visualization settings to group data, the analyst makes the insights more accessible to stakeholders, facilitating faster decision-making and preventing cognitive overload caused by displaying hundreds of individual data points simultaneously.

Answer

Change the chart type to a scatter plot.

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Filter the dataset to only include the last seven days.

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Increase the chart size to fill the entire screen width.

Question 10

Which TWO of the following are valid ways to improve the performance of a slow-loading Databricks SQL dashboard? (Choose two)

Accepted Answer

Enable query result caching on the SQL warehouse.. Improving dashboard performance is critical for user adoption. By utilizing query result caching and optimizing the underlying SQL queries, analysts can significantly reduce latency. Caching stores the output of frequent queries, while query optimization ensures that the compute resources are used efficiently. These techniques are standard operational practices that ensure dashboard responsiveness, even when dealing with massive datasets common in modern data lakehouses.

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Convert all visualizations into a single complex query.

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Add more users to the dashboard to increase processing power.

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Switch the dashboard to manual refresh mode only.

Question 11

Which visualization type is most appropriate for showing the distribution of a single continuous variable, such as the spread of customer ages?

Accepted Answer

Histogram. A histogram is the standard tool for visualizing the distribution of numerical data. It groups continuous values into ranges, or 'bins', and displays the frequency of observations in each bin. This allows analysts to quickly identify the shape of the data, such as whether it is normally distributed, skewed, or contains outliers, which is foundational for exploratory data analysis and effective business reporting.

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Pie Chart

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Line Chart

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Scatter Plot

Question 12

An analyst wants to publish a dashboard for the executive team. The dashboard contains sensitive data that should only be accessible to specific users. What is the correct way to handle access control?

Accepted Answer

Use the 'Share' button to grant 'Can View' access to authorized groups.. Managing dashboard permissions via the Workspace sharing settings is the standard approach to secure sensitive data. By assigning 'Can View' or 'Can Edit' roles based on user groups, the analyst ensures that only authorized individuals see the dashboard. This is a fundamental skill for data governance, ensuring that insights remain secure and compliant while still being available to the relevant stakeholders within the organization.

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Hard-code the security logic into the SQL queries.

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Export the dashboard as a PDF and email it to the team.

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Make the dashboard public and add a password to the title.

Question 13

Which THREE of the following steps are necessary to create an interactive dashboard in Databricks SQL? (Choose three)

Accepted Answer

Write a SQL query that includes filter parameters.. Building an interactive dashboard involves creating the underlying queries, selecting appropriate visualizations, and adding parameters for end-user interaction. Mastering this workflow is the core competency of a Databricks Data Analyst. These steps create a robust, self-service environment where users can filter and analyze data dynamically, reducing the burden on analysts to generate custom reports and empowering stakeholders to find their own answers to business questions.

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Define the visualization type in the SQL query code.

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Upload a CSV file containing the final report data.

Question 14

Refer to the exhibit. An analyst notices that widget 2 is displaying incorrect trends. Based on the configuration provided, what is the most likely cause?

Accepted Answer

The query for widget 2 does not aggregate data to match the X-axis.. The configuration shows that both widgets share the same X-axis ('date'), but they are visualizing different metrics ('sales' vs 'revenue'). If the data is aggregated at different levels or filtered differently, the trends might appear conflicting. Identifying mismatches between data sources and visualization axes is a common troubleshooting step for analysts, ensuring that the dashboard accurately reflects the underlying business logic and prevents misleading interpretations of the reported data.

Answer

The widget type is set to 'line' instead of 'bar'.

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The X-axis for widget 2 is not properly aligned to the date range.

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The 'revenue' column is incompatible with the 'line' chart type.

Question 15

Which feature in Databricks allows an analyst to automatically send a dashboard snapshot to a stakeholder's email on a recurring schedule?

Accepted Answer

Dashboard Subscriptions. The 'Subscription' feature in Databricks SQL is specifically designed for this purpose. It automates the distribution of dashboard results, which is a key requirement for keeping stakeholders informed without manual intervention. This feature ensures that critical business insights are delivered consistently, increasing the visibility of data-driven findings and ensuring that team members stay updated even if they do not log into the Databricks workspace daily.

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Alerts

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Query Snapshots

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Data Refresh Jobs

Question 16

When creating a dashboard, an analyst needs to compare two different categories side-by-side. Which visualization is best suited for this task?

Accepted Answer

Grouped Bar Chart. A grouped bar chart is the most effective visualization for comparing distinct categories side-by-side. It allows the viewer to easily perceive differences in magnitude between related items. This is a common requirement in business reporting, where analysts must compare performance across regions, products, or time periods. By selecting the right visualization, the analyst reduces the cognitive effort required for the audience to extract insights from the data.

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Heatmap

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Bubble Chart

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Stacked Area Chart

Question 17

Which of these is a benefit of using a 'parameter' in a Databricks SQL query?

Accepted Answer

It allows users to dynamically filter results without changing the code.. Parameters enable dynamic query execution. By allowing users to provide input at runtime, the same query can serve multiple analytical needs. This reduces the need to create dozens of near-identical queries, simplifying the codebase and making the dashboard much more interactive. It empowers the end-user to drill down into the specific segments they are interested in, which is a key driver for self-service analytics adoption within organizations.

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It permanently changes the data in the underlying table.

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It forces every user to run the exact same query.

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It creates a copy of the database for each user session.

Question 18

Which TWO of the following are true about Databricks SQL Alerts? (Choose two)

Accepted Answer

Alerts can trigger external workflows using webhooks.. Alerts in Databricks SQL are a critical component of a proactive monitoring strategy. By tracking the results of a query, they provide timely notifications that help analysts and business users respond to events immediately. Understanding the configuration of thresholds and notification channels is essential for building a reliable operational dashboard suite that moves beyond passive data reporting to active, event-driven data management.

Answer

Alerts only function if the dashboard is currently open.

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Alerts automatically delete data that triggers the condition.

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Alerts are only compatible with Python-based notebooks.

Question 19

You are visualizing server error logs over time. You want to highlight days where the error count exceeds a specific threshold (e.g., 50 errors). Which visualization feature is best for this?

Accepted Answer

Use conditional formatting on the data series.. Conditional formatting is the standard tool for data highlighting. By applying rules to the visualization, the analyst can automatically change colors or icons based on the data values. This 'at-a-glance' identification of anomalies is crucial for operational monitoring, as it allows users to pinpoint problematic timeframes immediately, reducing the mean time to detect issues without requiring the user to inspect every individual data point manually.

Answer

Add a trend line to the chart.

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Change the chart type to a pie chart.

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Filter out all days with fewer than 50 errors.

Question 20

An analyst is preparing a report and needs to ensure that the dashboard data is always current. What is the most efficient way to achieve this?

Accepted Answer

Set a refresh schedule on the dashboard.. Setting a regular refresh schedule is the correct way to ensure data remains fresh. This automation removes the manual effort of refreshing queries, ensuring that the dashboard always represents the latest state of the business. It is a fundamental operational task that guarantees the dashboard's reliability and relevance, preventing stakeholders from making decisions based on stale or outdated information, which is a major risk in any data-driven environment.

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Manually refresh the dashboard every morning.

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Re-run the SQL queries in a notebook and export the results.

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Ask the SQL warehouse to stay running indefinitely.