Salesforce Certified Tableau Data Analyst Practice Exam

A data analyst needs to create a calculated field that shows the running total of sales across months in a Tableau view.

What type of calculation should the analyst use?

1. A Level of Detail (LOD) expression
2. A basic SUM aggregation
3. A parameter-based calculation
4. A table calculation using RUNNING_SUM(SUM([Sales])) that computes the cumulative total across the table’s partition and addressing dimensions

An analyst needs each customer’s total annual purchases to remain the complete annual figure even when the dashboard is filtered to a specific product category.

What Tableau feature should the analyst use?

1. A FIXED Level of Detail expression: {FIXED [Customer ID] : SUM([Sales])}, which calculates the total at the customer level independent of the view’s dimensions and filters
2. A data source filter on Customer ID
3. A table calculation with WINDOW_SUM
4. A standard SUM([Sales]) measure

An analyst is connecting to a large SQL Server database with 500 million rows, and building visualizations with a live connection is slow.

What data strategy should the analyst implement?

1. Switch to a flat CSV file export of the data
2. Reduce the database to 1 million rows
3. Create a Tableau extract with appropriate aggregation and filtering to reduce data volume, schedule incremental refreshes to maintain freshness, and use extract-based calculations for better performance
4. Always use live connections for real-time accuracy

An analyst needs to combine data from two different data sources — a SQL database and a Google Sheet — in a single Tableau workbook.

What Tableau feature should the analyst use?

1. This is not possible in Tableau
2. Export both sources to CSV and join them in Excel first
3. Copy data from one source into the other before connecting
4. Use a cross-database join or data blending — cross-database join for row-level matching, or blending for aggregate-level combination via a linking field

An analyst wants to let users dynamically switch a chart between viewing revenue, profit, or quantity without creating three separate charts.

What Tableau technique should the analyst use?

1. Use a filter on the Measure Names dimension
2. Create a parameter listing the measure options and a calculated field that returns the selected measure’s value, then use the parameter to control what the chart displays
3. Create three charts and hide/show them with dashboard actions
4. Build the chart with all three measures as separate axes

An analyst’s scatter plot with 1 million data points renders slowly and the marks are indistinguishable.

What visualization technique should the analyst apply?

1. Change to a pie chart to avoid the point overload
2. Increase the chart size to accommodate more points
3. Remove data points until the chart renders quickly
4. Apply mark-density techniques: transparency to reveal overlap, aggregation to meaningful groupings, hexbin/density plots, or statistical sampling of a representative subset

An analyst needs to show the top 10 products by revenue in a bar chart, plus all remaining products grouped as ‘Other’.

How should the analyst create this view?

1. Create a separate chart for ‘Other’ products
2. Manually type the top 10 product names into a set
3. Use RANK() or a set to identify the top 10, a calculated field that labels products 1–10 by name and all others as ‘Other’, and an LOD/table calculation to sum the Other group
4. Filter to only the top 10 and ignore the rest

An analyst wants to highlight, on a monthly revenue line chart, the months where revenue exceeded target with a different color.

What Tableau feature should the analyst use?

1. Add reference lines for the target without conditional coloring
2. Create a calculated field comparing revenue to target (IF SUM([Revenue]) > SUM([Target]) THEN ‘Above’ ELSE ‘Below’ END) and place it on the Color shelf to dynamically color the marks
3. Use dashboard annotations to label months above target
4. Manually change the color of individual data points after building the chart

An analyst receives a dataset with date values stored as strings in YYYY-MM-DD format, and Tableau is not recognizing them as dates.

How should the analyst convert these to proper date fields?

1. Use DATEPARSE() for the custom format, or change the field’s data type to Date, or use DATE()/MAKEDATE() in a calculated field to construct the date
2. Delete the date column and recreate it in the data source
3. Accept that Tableau cannot handle string dates
4. Manually retype every date value in the correct format

An analyst in Tableau Prep needs to merge two input files with slightly different column names and formats into a unified dataset.

What Tableau Prep operations should the analyst use?

1. Open both files in Excel and copy-paste them together
2. Connect only to one file and ignore the other
3. Manually rename columns in the source files before connecting
4. Use a Union step to combine the files vertically, then Rename and Clean operations to standardize column names, plus data-type conversions to align formats into a unified output