Tableau TDA-C01 Exam Dumps & Practice Test Questions

Question 1:

You are the owner of a Tableau alert and receive an email stating that the alert has been suspended. Where can you go to resume this suspended alert?

A The My Content section on Tableau web pages
B The Data Source page in Tableau Desktop
C The Notifications section in Tableau Prep
D The Shared with Me page

Correct answer: A

Explanation:

In Tableau, alerts are automated notifications that activate when certain conditions or thresholds in your data dashboards are met. When an alert is suspended, it means that its triggering conditions are temporarily paused, and the alert will no longer send notifications until it is resumed. If you are the alert owner and want to reactivate it, you must access the correct management interface.

The My Content area within Tableau's web interface is the designated place for users to manage content they own or have created. This includes alerts, data sources, and workbooks. Since alerts are user-specific items, they are controlled in the user’s personal content space, making it the ideal location to resume suspended alerts. You can edit, delete, or resume alerts directly here.

Option A is correct because the My Content page allows owners to handle their alerts fully, including resuming any that have been paused or suspended.

Option B, the Data Source page in Tableau Desktop, is not the right place for alert management. Tableau Desktop primarily focuses on creating and interacting with data sources and visualizations, but alerts are managed via the web interface.

Option C refers to the Notifications area in Tableau Prep, which handles notifications related to data preparation workflows, not dashboard alerts.

Option D, the Shared with Me page, displays content shared by others, but it does not allow managing personal alerts that you own.

Therefore, the correct location to resume a suspended alert is the My Content area on Tableau’s web pages.

Question 2:

Given a data source in Tableau Server, you want to make sure the data refreshes every hour. Which option should you choose to schedule this update?

A Extract Refreshes
B New
C Connected Workbooks
D Connections

Correct answer: A

Explanation:

On Tableau Server, when working with data extracts—which are snapshots of data imported into Tableau—you often need to refresh these extracts regularly to ensure the data remains current. To automate this, Tableau provides a feature called Extract Refreshes that allows you to set up scheduled refreshes.

Selecting Extract Refreshes enables you to create a schedule that updates the data extract at defined intervals. For example, setting this to refresh every hour ensures that the data shown in your Tableau dashboards is refreshed hourly with the latest data from your source.

Option A is the correct choice because it directly controls the refresh schedule of the data extracts.

Option B (New) is generally used to create a new data source or item, not for managing refresh schedules.

Option C (Connected Workbooks) shows which Tableau workbooks are connected to the data source but does not provide control over extract refresh scheduling.

Option D (Connections) displays active connections to live data sources or extracts but doesn’t handle scheduled updates.

In summary, Extract Refreshes is the feature designed specifically to automate data updates on a set schedule, such as hourly, making it the appropriate selection to keep your Tableau Server data source up to date.

Question 3:

You have a dashboard that currently includes one sheet. Your goal is to replace the existing sheet with Sheet2. What is the appropriate method to do this?

A. Right-click Sheet2 and choose Add to Dashboard
B. Select Sheet3 and use the Swap Sheet button next to Sheet2
C. Use the context menu on Sheet3 to Remove Dashboard Item
D. Drag Sheet2 onto the dashboard
E. From the context menu of Sheet3, select Deselect

Correct Answer: D

Explanation:

In dashboard design tools like Tableau, replacing an existing sheet with another involves adding the new sheet onto the dashboard and either removing or overwriting the current one. The simplest and most direct method is to drag the new sheet into the dashboard area where you want it to appear. By dragging Sheet2 onto the existing dashboard, you effectively replace the previous sheet, assuming the layout allows it.

Option A suggests adding Sheet2 by right-clicking and selecting “Add to Dashboard.” While this action adds Sheet2, it does not replace the existing sheet automatically. You would need to manually remove the original sheet afterward, so this doesn’t fully solve the problem.

Option B refers to swapping Sheet3 with Sheet2 using a “Swap Sheet” button. Although some tools might offer a swap feature, this is not a standard or widely available method and the question’s wording doesn’t confirm such a feature exists.

Option C involves removing Sheet3 from the dashboard. This only deletes the existing sheet and doesn’t bring Sheet2 into the dashboard, which means the goal of replacement is incomplete.

Option E suggests deselecting Sheet3, which only removes the selection highlight or focus from the sheet but does not change the dashboard content or replace any sheets.

Therefore, D is the correct choice because dragging Sheet2 onto the dashboard is the most straightforward way to replace the current sheet, streamlining the update process without extra manual steps. It directly targets the replacement action in one simple operation, which is the most efficient approach in dashboard management.

Question 4:

You have a primary data source with a dimension called Dorm_Code and a secondary data source containing updated dorm codes. 

You want to import the updated codes into Tableau and have them used in existing visualizations, keeping the original field name Dorm_Code. What should you do?

A. Import the secondary data source as a union.
B. Import the secondary data as a left join to the primary data, then create a calculated field for Dorm_Name using the new codes.
C. Use relationships to link the secondary data source, then replace references of Dorm_Name with Dorm_Name_New in the Data pane.
D. Use data blending and edit primary aliases to replace the primary data source’s alias values with those from the secondary data.

Correct Answer: C

Explanation:

When you need to integrate updated values from a secondary data source into an existing primary data source in Tableau while maintaining the same field name for consistent visualization use, relationships are the best approach. Relationships allow you to connect tables dynamically without physically merging data upfront, enabling Tableau to perform joins at query time with more flexibility.

Option A suggests importing the secondary source as a union, which stacks data vertically by adding rows. This method does not replace or update existing values in the dimension Dorm_Code but merely appends additional records. Since the goal is to update the existing codes, a union is not appropriate.

Option B involves performing a left join between the secondary and primary data, then creating a calculated field to replace Dorm_Name with the updated values. Although possible, this method is cumbersome and less dynamic because it requires manual calculation and does not fully leverage Tableau’s newer relationship model, which automatically manages data integration more efficiently.

Option D suggests using data blending and editing aliases. Data blending is useful for combining data at the visualization level but does not modify the underlying data model or update field values dynamically. Changing aliases would only rename display values, not update the core Dorm_Code field across all uses.

Option C is the correct choice because relationships let you link the secondary data source based on common fields without physically merging datasets. Once linked, you can right-click the Dorm_Name field in the Data pane and replace all references with Dorm_Name_New from the secondary data source. This approach keeps your existing visualizations intact and dynamically reflects updated codes while maintaining the original field name Dorm_Code, ensuring consistency and flexibility across your workbook.

Question 5:

You want to build a dual-axis chart in Tableau consisting of a shape chart and a line chart, both using the same measure named Population on the axes. 

How can you make the shapes significantly larger than the line marks?

A. Duplicate the Population measure, place the copy on the second Marks card, and adjust the mark sizes independently.
B. On the second axis, select Shape on the Marks card, choose Custom Shape Palette, and click Reset.
C. Design a custom shape larger than the default and add it to the Shapes folder in My Repository.
D. Convert Population to a discrete dimension.

Correct Answer: A

Explanation:

When creating dual-axis charts in Tableau, controlling the size of marks for each axis independently is essential to achieve clear visual distinction between the two data representations. In this case, the goal is to have shapes much larger than the line marks, both representing the Population measure but in different visual styles.

Option A correctly describes the method needed to achieve this. By duplicating the Population measure and placing the duplicate on the second Marks card, you effectively create two independent layers for the dual-axis chart—one controlling the line chart and the other controlling the shape chart. This setup allows you to adjust the size settings separately on each Marks card, enabling you to make the shapes significantly larger than the lines, providing the desired visual emphasis.

Option B relates to selecting and resetting the shape palette but does not address size differences between the marks. Changing the shape style alone will not make shapes larger relative to the line chart.

Option C involves creating custom shapes that might be visually bigger, but this approach is static and doesn’t give you the flexibility to dynamically resize marks through Tableau’s size controls. It is more suited for changing the design of the shape rather than its size relative to other marks.

Option D involves converting the Population measure to a discrete dimension, which affects how Tableau aggregates and displays data but does not directly affect mark sizes or solve the issue of making shapes larger than lines.

In summary, duplicating the Population measure and assigning it to the second Marks card, then adjusting sizes independently (Option A), is the best way to control mark sizes in a dual-axis chart and make shapes larger than lines.

Question 6:

You have a line chart and want to add a comment or note specifically for the data point in March 2020, similar to the example visualization. What is the best way to do this?

A. Drag the growth rate field to the Text area on the Marks card.
B. Add a tooltip to the chart.
C. Use the Annotate feature on the March 2020 data point.
D. Enable captions for the worksheet.

Correct Answer: C

Explanation:

Adding a specific comment or note tied to a particular data point on a chart is best accomplished by using an annotation. In Tableau and many other visualization tools, annotations allow users to directly label or comment on specific points, making it easy to call attention to key information—such as the data for March 2020 in a line chart.

Option C is the best choice because annotating the March 2020 point places a visible text note directly on the chart at the exact location of the data point. This ensures the comment is clearly associated with that point and remains visible without requiring user interaction.

Option A suggests dragging the growth rate field to Text on the Marks card. This action would display the growth rate as a label for all data points or the current mark, but it doesn’t provide a targeted comment or note specifically calling out March 2020.

Option B, adding a tooltip, allows users to see information when they hover over a data point, but tooltips do not appear persistently on the chart. Tooltips are interactive rather than static annotations, so they cannot serve as permanent comments.

Option D refers to enabling captions, which are generally used to provide descriptions or summaries of the entire visualization or worksheet. Captions do not allow commenting on individual points within the chart.

Therefore, Option C—using annotations—is the ideal method to add a comment specifically to the March 2020 point, ensuring clarity and emphasis on the data of interest.

Question 7:

You have a dataset and want to create a visualization that displays the relative share of each category as part of the whole. 

Which quick table calculation should you apply to the Text on the Marks card to achieve this?

A. Percent of Total
B. Percent Difference
C. Compound Growth Rate
D. Percentile

Correct answer: A

Explanation:

When working with data visualizations, one common goal is to illustrate how individual parts contribute to the whole. The quick table calculation called "Percent of Total" is designed precisely for this purpose. It calculates each value as a percentage of the sum of all values, making it perfect for scenarios where you want to show the proportional contribution of each category or item within the dataset.

For example, if you are visualizing sales figures across different regions, applying Percent of Total will reveal how much each region contributes to overall sales, clearly displaying the relative importance or weight of each category.

On the other hand, Percent Difference calculates the change between values—usually over time or between two points—and is more suited for analyzing growth or decline, not proportions. Therefore, it wouldn’t be appropriate if your goal is to display parts of a whole.

The Compound Growth Rate is a calculation used to measure the average growth rate over multiple periods, typically used in financial or time-series data. It doesn’t serve to show the breakdown of a total value by parts, so it’s unrelated to the question’s requirement.

Lastly, Percentile ranks values relative to a distribution, indicating the position of each value compared to the entire dataset. This is useful for statistical ranking but does not translate into percentage shares of a total.

Thus, to effectively communicate how each category fits into the overall sum, the Percent of Total calculation is the ideal choice. It transforms raw values into intuitive percentage shares, making it easy for users to grasp proportions at a glance.

Question 8:

You’ve been given access to a folder with the following files: Sales.csv, Book1.twb, Sales.hyper, and Export.mdb. Which file among these is a data extract?

A. Export.mdb
B. Sales.hyper
C. Book1.twb
D. Sales.csv

Correct answer: B

Explanation:

In the realm of data analytics, particularly when using Tableau, the term data extract refers to a specially optimized snapshot of data taken from a source to improve performance during analysis. Let’s examine each file type to identify which one represents a data extract.

Starting with Sales.csv: This is a plain-text file containing comma-separated values. It stores tabular data but lacks any performance optimization or indexing. CSV files are raw data files and do not qualify as extracts.

Next, Book1.twb is a Tableau workbook file. It stores the structure of your Tableau report including sheets, dashboards, and visual layout instructions. However, it does not contain actual data but instead connects to a data source, which can be a live connection or an extract. Therefore, it is not an extract itself.

Export.mdb is a Microsoft Access database file, containing tables and queries in a relational database format. Although it stores data, it is a database file, not a Tableau extract.

Finally, Sales.hyper is a file specific to Tableau’s newer extract format. The .hyper file is designed to store data extracts in a highly efficient, compressed format that Tableau uses to accelerate querying and improve performance. Extracts in this format allow users to work offline or with snapshots of the data, which is critical for faster reporting and analysis.

In summary, among the listed files, only Sales.hyper is a Tableau data extract file, containing a snapshot of data optimized for performance, making option B the correct answer.

Question 9:

While analyzing a dataset in Tableau, you want to display the percentage that each product category contributes to the overall sales total. 

Which Tableau functionality is best suited for calculating and displaying these percentage values?

A. Quick Table Calculation
B. Calculated Field
C. Parameter
D. Data Blending

Correct answer: A

Explanation:

In Tableau, when you want to quickly show the proportion of each category relative to the total, Quick Table Calculations are the most straightforward and efficient tool to use. Specifically, the "Percent of Total" calculation is designed for this exact purpose—it automatically computes the percentage contribution of each data point relative to the sum of all points in a given partition.

Quick Table Calculations are accessible directly from the drop-down menu on a measure within Tableau, allowing users to apply common calculations without writing complex formulas. Once applied, Tableau recalculates the values based on the context of the visualization, making it easy to see the relative importance of each category, such as product categories contributing to total sales.

Option B (Calculated Field), while powerful for custom calculations, requires manually writing the formula and is not as quick or intuitive for simple percentage-of-total calculations as the built-in quick table calculation feature.

Option C (Parameter) is used to create interactive controls or input variables that users can change to affect the view, but it doesn’t inherently perform percentage calculations.

Option D (Data Blending) refers to combining data from multiple sources and is unrelated to calculating percentages within a single dataset.

Using Quick Table Calculations allows analysts to rapidly understand and visualize the share of each category in the dataset, making insights clearer without additional setup or formula complexity. This feature is a key tool for data analysts aiming for efficiency and clarity in their Tableau visualizations.

Question 10:

When creating a map visualization of sales data broken down by regions in Tableau, how can you ensure that each region is represented by a distinct color to differentiate them clearly?

A. Create a calculated field to assign specific colors to each region.
B. Place the "Region" field on the Color shelf.
C. Use a parameter to select which regions to color.
D. Add the "Region" field to the Detail shelf for individual coloring.

Correct answer: B

Explanation:

In Tableau, differentiating categories such as regions by color is most efficiently done by dragging the categorical field (in this case, "Region") to the Color shelf on the Marks card. This automatically assigns a unique color to each distinct region value in the dataset, helping users visually separate regions on the map.

This functionality is fundamental to Tableau’s design, as it simplifies the creation of visually distinct groups in charts and maps. The color legend that appears allows viewers to quickly identify each region by color, which enhances the map's readability and effectiveness.

Option A (Calculated Field) could be used if you want to assign very specific or customized colors, but it is more complex and unnecessary when Tableau’s automatic coloring suffices.

Option C (Parameter) lets users dynamically change inputs or filter data but is not a direct method to color regions distinctly.

Option D (Detail shelf) adds granularity to the view by breaking marks into finer groups but does not control color assignment. While it provides additional detail, it won’t automatically assign distinct colors to each region.

By simply placing the "Region" field on the Color shelf, Tableau leverages its built-in color palettes and provides an immediate, clear distinction among different regions, making this the simplest and most effective solution for map visualization clarity.