Free Tableau TDA-C01 Practice Exam with Questions & Answers | Set: 3

To share a dashboard that contains confidential information about patients health and needs to always be up to date for a team of healthcare workers, you should publish it to Tableau Server. This will allow you to control who can access and interact with your dashboard, as well as schedule automatic refreshes of your data source or extract. Publishing to Tableau Public would expose your data to anyone on the internet, printing to PDF would not update your data, and exporting as a .twbx would require sending a large file that might not be compatible with other versions of Tableau. References: https://help.tableau.com/current/pro/desktop/en-us/publish_workbooks_share.htm https://help.tableau.com/current/pro/desktop/en-us/publish_workbooks_tableauserver.htm

When sharing sensitive and confidential information that needs to be kept up-to-date, such as patient health data for a team of healthcare workers, the best practice is to publish the dashboard to Tableau Server. Tableau Server is designed for secure, controlled access and can be configured to ensure that data is refreshed as needed. Publishing to Tableau Public is not secure for confidential data, printing to PDF or exporting as a .twbx does not allow for live data updates.

To configure the shapes to be much larger than the line, you need to duplicate Population and drag it to the second Marks card. This will create a dual axis chart with two measures on one axis. You can then select Shape on one Marks card and Line on another Marks card, and adjust the size of each mark independently using the Size slider or menu. References: https://help.tableau.com/current/pro/desktop/en-us/multiplemeasures_dualaxes.htm https://help.tableau.com/current/pro/desktop/en-us/marks_markproperties_size.htm

In a dual-axis chart in Tableau, if you want to have two different visual mark types (like a shape and a line) and configure them differently (such as making one larger than the other), you would need to duplicate the measure. You then drag this duplicate to the second Marks card (which represents the second axis). There, you can adjust the size of the marks (shapes, in this case) independently of the line marks on the first Marks card.

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

The two formulas that will return a value of true if a month has sales greater than $50,000, otherwise the formula must return a value of false, are:

B. IIF(SUM([Sales]) > 50000, TRUE, FALSE) D. [Sales] > 50000

The IIF function is a logical function that returns one value if a condition is true, and another value if the condition is false. In this case, the condition is SUM([Sales]) > 50000, which means that the sum of sales for a month is greater than $50,000. The function will return TRUE if the condition is true, and FALSE if the condition is false.

The comparison operator > is a logical operator that returns TRUE if the left operand is greater than the right operand, and FALSE otherwise. In this case, the operands are [Sales] and 50000, which means that the sales for a month are greater than $50,000. The operator will return TRUE if the sales are greater than $50,000, and FALSE otherwise.

The other options are not correct for this scenario. The IN function is a logical function that returns TRUE if a value is in a set of values, and FALSE otherwise. In this case, the value is SUM([Sales]), which is not in the set of values (50000). The function will always return FALSE. The MAX function is an aggregation function that returns the maximum value in a field or expression. In this case, the field or expression is [Sales], 50000, which is not valid syntax. The function will return an error. References: https://help.tableau.com/current/pro/desktop/en-us/functions_functions_logical.htm#IIF https://help.tableau.com/current/pro/desktop/en-us/operators.htm https://help.tableau.com/current/pro/desktop/en-us/functions_functions_aggregate.htm#MAX

To connect to a tab-delimited data file named sales.tsv, you should select the option To a File > Text file in Tableau Desktop. This option will allow you to browse and select the file from your local or network drive and connect to it as a data source. You can then specify the text file properties, such as the delimiter, the text qualifier, and whether the first row contains column names. You can also join, union, or blend the data with other data sources if needed. References:

• Text File - Tableau
• Tableau Certified Data Analyst Study Guide

Data interpreter is a feature that helps you clean and structure your data in Excel before importing it into Tableau. It detects and removes any headers, footers, subtotals, or other elements that are not part of the actual data. It also splits any merged cells and fills in any missing values. In this case, data interpreter provides the following results:

• It removes the first three rows and the last two rows that contain headers and footers.
• It splits the merged cells in column A and fills in the missing values with “Fiction” or “Non-Fiction”.
• It does not remove or change any other rows or cells.

Therefore, the number of rows of data that will be ingested into Tableau as values is 17, which is the number of rows left after removing the headers and footers. References: https://help.tableau.com/current/pro/desktop/en-us/importing_cleaning_up_data.htm https://help.tableau.com/current/pro/desktop/en-us/importing_data_interpreter.htm

Based on the Data Interpreter's results and the provided Excel dataset screenshot, the number of rows of data that will be ingested into Tableau as values is 17. The Data Interpreter has identified and excluded headers and footers, and the rows with actual data are considered for analysis.

Select

Where

The correct way to complete the query is:

SELECT * FROM [dbo] . [Students]

WHERE [Students].[City] = ‘Berlin’

The SELECT statement is used to retrieve data from a table or a view. The * symbol means that all columns will be returned. The FROM clause specifies the table or view name and the schema name.

The WHERE clause is used to filter the data based on a condition. The condition is [Students].[City] = ‘Berlin’, which means that only rows where the City column of the Students table is equal to ‘Berlin’ will be returned.

The other options are not relevant for this query. CAST is a function that converts a value from one data type to another. JOIN is a clause that combines data from two or more tables based on a common field. ON is a keyword that specifies the join condition. UNION is a set operation that combines the results of two or more queries into one result set.

References: https://docs.microsoft.com/en-us/sql/t-sql/queries/select-transact-sql?view=sql-server-ver15 https://docs.microsoft.com/en-us/sql/t-sql/queries/where-transact-sql?view=sql-server-ver15 https://docs.microsoft.com/en-us/sql/t-sql/functions/cast-and-convert-transact-sql?view=sql-server-ver15 https://docs.microsoft.com/en-us/sql/t-sql/queries/from-transact-sql?view=sql-server-ver15#join https://docs.microsoft.com/en-us/sql/t-sql/language-elements/set-operators-union-transact-sql?view=sql-server-ver15