Analytics-DA-201 Dumps with Practice Exam Questions Answers

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.

Publishing the Excel-based goals data as its own managed data source provides the most appropriate architecture for a file that must be updated regularly and reused alongside an existing published revenue source. The analyst can publish the Excel connection as an extract and configure the appropriate refresh mechanism, after which the dashboard workbook connects to both centrally governed published sources. Tableau documents that published extract data sources can be refreshed on a schedule and that every connected workbook then benefits from the refreshed data. Merely embedding an Excel extract in one workbook would couple refresh management to that workbook, while simply uploading a flat file through web authoring does not guarantee updates to the original changing file. Modifying the ERP system is unnecessary. Therefore, option D provides the intended reusable and maintainable Tableau design.

Based on the line chart provided, each point on the line chart represents the average sales for a given month, and the percentage values indicate the change in average sales compared to the previous month. The month of November shows a significant increase from the month of October, indicated by a large upward spike on the chart and a high percentage increase.

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.