Get TDA-C01 Dumps : Verified Tableau Certified Data Analyst Exam

To combine the two tables that contain data about books in a library and ensure that all the data is retained, you should use a full outer join. A full outer join is a type of join that returns all rows from both tables, regardless of whether there is a match or not. If there is no match, null values are filled in for the missing fields.

To perform a full outer join, you need to do the following steps:

• Connect to both tables as your data sources in Tableau. You can use either live or extract connections.
• Drag one table to the canvas and drop it on top of another table. This will create a join between them based on a common field.
• Click on the join icon between the tables and select Full Outer Join from the drop-down list. This will change the join type to full outer join and show all rows from both tables.
• Optionally, you can add or remove join clauses by clicking on Add or Remove buttons next to each clause. You can also change or rename fields by clicking on them.

The other types of joins are not correct for this scenario. An inner join returns only the rows that have a match in both tables, which will exclude any books that are missing from either table. A left join returns all rows from the left table and only the matching rows from the right table, which will exclude any books that are only in the right table. A right join returns all rows from the right table and only the matching rows from the left table, which will exclude any books that are only in the left table. References: https://help.tableau.com/current/pro/desktop/en-us/joining_tables.htm https://help.tableau.com/current/pro/desktop/en-us/join_types.htm

When combining two datasets that are both incomplete and where it's important to retain all data from both sources, a full outer join is appropriate. This type of join ensures that all records from both tables are included in the combined dataset, even if there are no matching records in the other table.

There are different ways to prepare the data in Tableau Prep, but one possible answer is:

Answer area:

• Join the tables.
• Aggregate the data by Sales Team.
• Add the following filter to the Employees table: Sale Team - Team Alpha.

 To analyze sales trends over time for every sales team, you need to combine the data from the Orders and Employees tables. You can do this by joining the tables on the Rep ID column, which is a common identifier for both tables. This will create a single table with all the columns from both tables. Next, you need to aggregate the data by Sales Team, which is the level of analysis you want. You can do this by grouping the data by the Sales Team column and applying an aggregation function (such as sum, average, count, etc.) to the other columns that you want to measure (such as Total Amount, Rep Tenure, etc.). This will create a summary table with one row for each sales team and the aggregated values for the other columns. Finally, you need to filter the data to focus on a specific sales team, such as Team Alpha. You can do this by adding a filter to the Employees table and selecting only the rows where Sales Team equals Team Alpha. This will remove the rows for other sales teams and keep only the data for Team Alpha.

References:

• [Tableau Certified Data Analyst Exam Prep Guide], page 9, section “Preparing Data”
• [Tableau Help: Join Your Data]
• [Tableau Help: Aggregate Your Data]
• [Tableau Help: Filter Your Data]

https://help.tableau.com/current/pro/desktop/en-us/parameters_swap.htm

This formula uses a parameter (Pick Measure) to switch between the Sales and Profit measures. When the user selects '1', it shows Sales, and when '2' is selected, it shows Profit. This dynamic switching is enabled by the Case function.

The correct order of the three actions is:

• Add the date to the Columns shelf. Add the segment and the sales to the Rows shelf.
• Aggregate the date to month and year.
• From the Analytics pane, drag Forecast to the worksheet.

The first action is to add the date to the Columns shelf and the segment and the sales to the Rows shelf. This will create a line chart that shows the daily sales by segment over time. You can use the Show Me menu to choose a line chart if it is not selected by default.

The second action is to aggregate the date to month and year. This will group the daily sales into monthly sales and show the yearly trend. You can right-click on the date field on the Columns shelf and select Month (January 2017) from the menu. You can also drag Year from the Dimensions pane to the Columns shelf before or after Month.

The third action is to drag Forecast from the Analytics pane to the worksheet. This will add a forecast that predicts future sales by segment based on historical trends. You can customize the forecast by clicking on it and using the options on the Marks card.

The other options are not relevant for this scenario. Adding a trend line would show a linear or nonlinear relationship between two measures, but not a prediction of future values. Creating a calculated field that uses the model quantile function would return a value from a statistical model based on a given quantile, but not a forecast.

References: https://help.tableau.com/current/pro/desktop/en-us/analytics.htm https://help.tableau.com/current/pro/desktop/en-us/buildmanual_shelves.htm https://help.tableau.com/current/pro/desktop/en-us/dates.htm https://help.tableau.com/current/pro/desktop/en-us/analytics_forecast.htm https://help.tableau.com/current/pro/desktop/en-us/functions_functions_statistical.htm#MODEL_QUANTILE