[Tableau] Terraform Associate - Tableau Certified Data Analyst ​ Exam Dumps & Study Guide

Free [Tableau] Terraform Associate - Tableau Certified Data Analyst ​ Practice Questions Preview

• Question 1

  You are the owner of an alert.
  You receive an email notification that the alert was suspended.
  From where can you resume the suspended alert?

  • A. The My Content area of Tableau web pages
  • B. The Data Source page of Tableau Desktop
  • C. The Notifications area of Tableau Prep
  • D. The Shared with Me page

  Correct Answer: A

  Explanation:

  Based on the available information, the AI agrees with the suggested answer A: The My Content area of Tableau web pages.
  The primary reason for this choice is that alert management, including resuming suspended alerts, is typically performed within the Tableau Server or Tableau Cloud environment, accessible through the "My Content" area. This area provides a centralized location for managing alerts and other content.
  The other options are not correct:

  • B: The Data Source page of Tableau Desktop is primarily for data connection and modeling, not alert management.
  • C: The Notifications area of Tableau Prep is related to data preparation workflows, not Tableau Server alerts.
  • D: The "Shared with Me" page displays content shared with the user but does not provide alert management capabilities.

• Question 2

  You have the following data source in Tableau Server.
  
  You need to ensure that the data is updated every hour.
  What should you select?

  • A. Extract Refreshes
  • B. New
  • C. Connected Workbooks
  • D. Connections

  Correct Answer: A

  Explanation:

  The question asks how to ensure data in a Tableau Server data source is updated every hour.
  
  The AI agrees with the suggested answer A, "Extract Refreshes."
  
  Reasoning:
  Extracts in Tableau are snapshots of data that can be refreshed on a schedule. This allows for near real-time updates without directly querying the underlying database every time a dashboard is accessed. Scheduling an extract refresh for every hour will ensure the data is updated as required.
  
  Why other options are not suitable:
  

  • B. New: This option doesn't relate to data refresh.
  • C. Connected Workbooks: While connected workbooks use the data source, they don't directly control the data refresh schedule.
  • D. Connections: This refers to the connections to the data sources, not the refresh schedule of extracts.

  Here's a breakdown of why Extract Refreshes are the correct approach:

  • Scheduling: Tableau Server allows scheduling extract refreshes, which directly addresses the requirement of updating the data every hour.
  • Performance: Extracts generally improve performance because Tableau queries the extract instead of the live database for visualizations.

• Question 3

  You have the following tiled dashboard that has one sheet.
  
  You want to replace the sheet with Sheet2.
  What should you do?

  • A. Right-click Sheet2 and select Add to Dashboard
  • B. Select Sheet3 and click the Swap Sheet button next to Sheet2
  • C. From the context menu of Sheet3. select Remove Dashboard Item
  • D. Drag Sheet2 to the dashboard
  • E. From the context menu of Sheet3: select Deselect

  Correct Answer: B

  Explanation:

  The AI agrees with the suggested answer, which is B.
  The reasoning for choosing option B is that Tableau offers a direct sheet swapping feature. By selecting the sheet to be replaced (Sheet3 in this case) and then interacting with the new sheet (Sheet2), a "swap" button appears, allowing for a direct replacement. This is the most efficient way to replace a sheet in a tiled dashboard.
  Options A, C, D, and E are incorrect. Option A would add Sheet2 to the dashboard without removing Sheet3. Option C involves removing Sheet3, which is a valid step but not the complete action needed for replacing it with Sheet2, making it less efficient than swapping. Option D is similar to A, it adds Sheet2 to the dashboard but does not replace Sheet3, which could mess up the dashboard design. Option E is not applicable in this scenario.

  • Sheet Swapping in Tableau, Not Available: https://community.tableau.com/s/question/0D54T00000C6W9pSAF/sheet-swapping-in-tableau

• Question 4

  You have the following primary data source that contains a dimension named Dorm_Code.
  
  You receive the following secondary data source that contains updated dorm codes.
  
  You need to bring the updated dorm codes into Tableau and use the codes in existing visualizations. The new dorm codes must use the existing field name of Dorm_Code.
  What should you do?

  • A. Bring in the secondary data source as a union.
  • B. Bring in the secondary table as a left join to the primary data source. From the Data Source page select Create Calculated Field in the Dorm_Name field and enter [Dorm_Name_New] in the calculation window.
  • C. Bring in the secondary data source by using relationships. From the Data pane, right-click Dorm_Name. select Replace References and then select Dorm_Name_New.
  • D. Create a data blend and select Edit Primary Aliases to replace the primary data source alias values with values from the secondary data source.

  Correct Answer: C

  Explanation:

  The AI recommends answer D.
  Reasoning: The primary goal is to update existing dorm codes within existing visualizations while maintaining the original field name "Dorm_Code." Data blending, specifically using Edit Primary Aliases, allows for replacing alias values in the primary data source with values from the secondary data source. This approach directly addresses the requirement of updating the codes in place without altering the underlying field names or requiring extensive restructuring of the data model. Data blending is suited for this scenario because we are essentially correcting or augmenting data in the primary source with information from a secondary source based on a common dimension (Dorm_Name).
  Why other options are not suitable:

  • A: Bring in the secondary data source as a union. Unions append data vertically. This would duplicate the Dorm_Name values from the primary data source with new codes from the secondary and it's not suitable for updating the Dorm_Code directly. It requires creating a calculated field to prioritize the new values, which is less efficient.
  • B: Bring in the secondary table as a left join to the primary data source. From the Data Source page select Create Calculated Field in the Dorm_Name field and enter [Dorm_Name_New] in the calculation window. A left join would bring in the new dorm codes, but then creating a calculated field is an indirect and less efficient method. It also doesn't directly replace the values in the existing Dorm_Code field.
  • C: Bring in the secondary data source by using relationships. From the Data pane, right-click Dorm_Name. select Replace References and then select Dorm_Name_New. Replace References changes the underlying field used. Relationships are suitable for defining connections between tables based on related fields, not for replacing existing values or aliases. Moreover, "Replace References" would replace the entire Dorm_Name field with Dorm_Name_New, changing the field name, which is not what the question asks for. Relationships are better suited for connecting multiple data sources based on related fields.

  </

• Question 5

  You plan to create a visualization that has a dual axis chart. The dual axis chart will contain a shape chart and a line chart. Both charts will use the same measure named Population on the axis.
  You need to configure the shapes to be much larger than the line.
  What should you do?

  • A. Duplicate Population Drag the duplicate to the second Marks card and configure the size of the marks independently
  • B. For the second axis, select Shape on the Marks card. From Select Shape Palette, select Custom, and then select Reset
  • C. Create a custom shape that is larger than the default shape and add the shape to the Shapes folder in My Repository.
  • D. Change Population to a discrete dimension.

  Correct Answer: A

  Explanation:

  The suggested answer A is correct. To create a dual-axis chart with a shape chart and a line chart using the same measure (Population) and configure the shapes to be much larger than the line, you need to duplicate the Population measure, drag the duplicate to the second Marks card, and configure the size of the marks independently. This allows you to control the size of the shapes on one axis without affecting the line on the other axis.
  
  Here's a detailed breakdown:

  • Why A is correct:
    • Duplicating the 'Population' measure and placing it on the second Marks card allows for independent control over the visual properties (size, shape, color, etc.) of the shape chart without affecting the line chart.
    • By adjusting the "size" option on the Marks card associated with the shape chart, you can make the shapes significantly larger than the line.
  • Why other options are incorrect:
    • B: Selecting "Shape" on the Marks card and resetting the palette does not directly address the need to increase the size of the shapes relative to the line. It only changes the type of mark used.
    • C: Creating a custom shape and adding it to the repository only changes the shape being used, not its size relative to the line chart. While you could create a larger shape, directly manipulating the size on the Marks card is more straightforward.
    • D: Changing Population to a discrete dimension would fundamentally alter the nature of the chart, likely making it categorical rather than continuous. This would not achieve the desired dual-axis visualization.

  The core of the solution lies in independently controlling the size property of the shape chart on the dual axis, which is achieved through duplication and separate configuration.

  Citations:

  • Tableau Help: Dual Combination Charts, https://help.tableau.com/current/pro/desktop/en-us/buildexamples_dualcombination.htm
  • Tableau Community Forums: Dual Axis Different Mark Types, https://community.tableau.com/s/question/0D54T00000C6Y1DSA3/dual-axis-different-mark-types

• Question 6

  DRAG DROP -
  Your company has two divisions located in the United States and Germany. Each division has a separate database.
  The following is a sample of the sales data for the division in the United States.
  
  The following is a sample of the sales data for the division in Germany.
  
  You have population data in a file named Population.csv. The following is a sample of the data.
  
  You plan to create a visualization that will show the sales per capita for each salesperson. Each salesperson must be identified by their full name.
  You need to prepare the data to support the planned visualization. The data source must support being published.
  Which four actions should you perform in order?
  (Place the four correct options in order Use the arrows to move Options to Answer Area. Use Answer Area arrows to re-order the options.)
  

  Correct Answer: See interactive view.

  Explanation:

  Based on the question and discussion, the suggested answer requires modification to align with best practices for data preparation and visualization in Tableau. The core issue is efficiently combining and cleaning the data from different sources before calculating sales per capita and creating the visualization. The suggested approach of using Tableau Prep is valid; however, the order of operations and specific transformations need to be refined.

  Recommended Steps:

  1. Open Tableau Prep and connect to the United States and Germany sales data sources. This allows you to access and transform the data from both divisions within the Tableau Prep environment.
  2. Union the sales data from the United States and Germany divisions. A union combines the rows from both datasets into a single dataset. This is a crucial first step to consolidate the sales information.
  3. Create a calculated field to standardize the 'Name' field. The German data appears to have "FirstName LastName" while US Data appears to have "LastName, FirstName" , it is necessary to create a calculated field that concatenates the first and last names in the same format for both datasets (e.g., "FirstName LastName"). This will allow for a proper join later on and prevent duplicate entries based on name variations.
  4. Join the combined sales data with the Population data using the 'City' field as the join key. This merges the sales data with the population data, allowing you to calculate sales per capita. It's essential to ensure that the city names are consistent across both datasets (e.g., "New York" vs. "New York City"). Cleaning and standardizing the city names might be necessary before the join.

  Reasoning:

  • Data Consolidation: Combining the sales data through a union operation is necessary to create a unified dataset for analysis. The sales per capita calculation requires all sales data to be in a single table.
  • Data Standardization: Standardizing the names to create a new column ensures data consistency between datasets.
  • Joining for Calculation: Joining the sales data with the population data on the 'City' field allows for the calculation of sales per capita, which is the key metric for the visualization.
  • Tableau Prep's Capabilities: Tableau Prep is specifically designed for data preparation tasks like cleaning, shaping, and combining data, making it the ideal tool for this scenario.

  Why the original suggested answer is not optimal:

  • Order of operations: Without first standardizing the Name field, the data cannot be accurately joined or analyzed to provide the visualization requested by the question.

  By following these steps, the data will be properly prepared and combined, allowing for accurate calculation of sales per capita and effective visualization in Tableau.

  In summary, the AI suggests a revised answer that emphasizes data cleaning and standardization steps to ensure an accurate calculation of sales per capita and effective visualization.

  
  

  Citations:

  • Tableau Prep Documentation, https://help.tableau.com/current/pro/desktop/en-us/prep_concepts_overview.htm
  • Tableau Prep Join Data, https://help.tableau.com/current/pro/desktop/en-us/prep_join.htm
  • Tableau Prep Union Data, https://help.tableau.com/current/pro/desktop/en-us/prep_union.htm

• Question 7

  DRAG DROP -
  You have the following dataset.
  
  You need to create the following worksheet.
  
  The table must show either profit or sales based on the selection from the Parameter 1 menu.
  Which three actions should you perform in order?
  (Place the three correct options in order. Use the arrows to move Options to Answer Area. Use Answer Area arrows to re-order the options.)
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  Reasoning: The suggested answer provides a step-by-step approach to dynamically display either profit or sales based on a parameter selection. This involves creating a parameter with 'profit' and 'sales' as options, creating a calculated field using an IF statement to switch between Sales and Profit based on the parameter's value, and then placing the calculated field on the Text mark and Region on the Rows shelf to build the desired table. This approach aligns with the typical method used in data visualization tools like Tableau to achieve dynamic data display based on user-selected parameters. It directly addresses the question's requirement to show either profit or sales based on the Parameter 1 menu.
  Why other options are not suitable: The question only provides one answer so there are no other options to discuss.

  Steps to implement the solution:
  

  • Create a Parameter: Define a parameter named "Parameter 1" with data type String and allowable values as a list: "profit" and "sales".
  • Create a Calculated Field: Create a calculated field, for example, "Profit or Sales", using the formula provided in the suggested answer. This formula uses a conditional statement to display Sales when 'sales' is selected in the parameter and Profit when 'profit' is selected.
  • Build the Visualization: Drag the "Profit or Sales" calculated field to the Text mark. Drag the "Region" dimension to the Rows shelf. Show the Parameter control so that it’s visible to the user.

  The formula in the calculated field ensures that the correct measure (Profit or Sales) is displayed based on the user's selection from the parameter control.

  The approach is consistent with standard practices in data visualization for creating interactive and dynamic reports.

  The suggested answer correctly outlines the necessary steps to achieve the desired outcome.

  
  Citations:

  • Tableau Help, Parameters, https://help.tableau.com/current/pro/desktop/en-us/parameters_create.htm

• Question 8

  You have a line chart on a worksheet.
  You want to add a comment to March 2020 as shown in the following visualization.
  
  What should you do?

  • A. Drag the growth rate to Text on the Marks card.
  • B. Add a tooltip.
  • C. Annotate March 2020.
  • D. Enable captions.

  Correct Answer: C

  Explanation:

  The AI agrees with the suggested answer, which is C. Annotate March 2020.
  The reason for choosing option C is that the desired outcome, adding a specific comment to a specific data point (March 2020) on a line chart, is precisely what the "Annotate" feature is designed for. This feature allows users to add targeted notes directly to visualizations, enhancing clarity and providing context to specific data points.
  The reasons for not choosing the other options are as follows:

  • Option A: Dragging the growth rate to Text on the Marks card would display the growth rate value, not a custom comment, and it might apply to all data points, not just March 2020.
  • Option B: Adding a tooltip would display information when hovering over the data point, not a persistent comment directly on the visualization. While tooltips are useful, they don't provide the always-visible annotation shown in the desired outcome.
  • Option D: Enabling captions provides a general description of the worksheet, not a specific comment on a particular data point.

  The 'Annotate' feature is the most direct and appropriate way to achieve the visualization shown in the question.
  

• Question 9

  You have the following dataset.
  
  You need to create the following visualization.
  
  Which quick table calculation should you drag to Text on the Marks card?

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

  Correct Answer: A

  Explanation:

  The AI agrees with the suggested answer, which is A. Percent of Total.
  
  Reasoning:
  The visualization aims to display each category's contribution to the overall total. The "Percent of Total" quick table calculation in Tableau directly computes this by showing each value as a percentage of the sum of all values. This aligns perfectly with the objective presented in the question.
  
  Reasons for not choosing the other options:
  

  • B. Percent Difference: This calculation shows the percentage change from a previous value, which is not relevant to showing the contribution of each category to the total.
  • C. Compound Growth Rate: This calculation is used to measure the growth rate over a period, not the proportion of each category.
  • D. Percentile: This calculation shows the relative rank of a value within a dataset, and not its contribution to the whole.

  Based on the above analysis, "Percent of Total" is the most appropriate quick table calculation to achieve the desired visualization.

• Question 10

  A colleague provides you with access to a folder that contains the following files:
  
  Sales.csv -
  
  Book1.twb -
  
  Sales.hyper -
  
  Export.mdb -
  Which file contains an extract?

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

  Correct Answer: B

  Explanation:

  The AI agrees with the suggested answer.
  The correct answer is B. Sales.hyper.
  Reasoning: When Tableau creates an extract of data, it saves it as a local file. The file extension for Tableau extracts is either .tde (older versions) or .hyper (Tableau 10.5 and later). The .hyper format is a high-performance data engine.
  Reasons for not choosing the other options:

  • A. Export.mdb: .mdb is the file extension for Microsoft Access databases, not Tableau extracts.
  • C. Book1.twb: .twb is the file extension for Tableau workbooks, which contain instructions for connecting to data sources and visualizations, not the extracted data itself.
  • D. Sales.csv: .csv is a comma-separated values file, a plain text format for storing tabular data. While Tableau can connect to .csv files, it is not a Tableau extract file.

  Citations:

  • Tableau Help: Extract Your Data, https://help.tableau.com/current/pro/desktop/en-us/extracting_data.htm