You have a workbook that connects to a database. The database requires authentication.
You plan to publish the workbook and schedule a daily refresh of the data.
Which two conditions must be met to schedule the refresh? Choose two.
To schedule a refresh of the data, you need to ensure that Tableau Server or Tableau Online can access the database without prompting for credentials. This can be done by embedding the credentials in the workbook or the published data source. You also need to extract the data from the database and publish it as a separate data source or as part of the workbook. A live connection would not allow you to refresh the data on a schedule, as it would always query the database directly. A hyper file is a format for storing extracted data, but it is not a requirement for scheduling a refresh.Reference:
Tableau Help: Embedding Database Credentials
Tableau Help: Extract Your Data
You have the following: Overall Rank and Rank are calculated fields that use the RANK function.
You filter out the sub-category where [Ovorall Rank] - 1.
For which three the sub-categories will the value of Rank change? Choose three.
In Tableau, the RANK function assigns a rank to each row within a partition of the data, based on the value of the field being ranked. It is important to understand that the rank is recalculated whenever the underlying data or the partitioning changes.
In the given scenario, the Overall Rank is based on the Sales figures, while the Rank (presumably) is based on the Sales within the Category. When filtering on the condition where [Overall Rank] - 1, it means we are excluding the sub-category that has an Overall Rank of 2.
Looking at the data:
Furnishings has an Overall Rank of 8, which does not meet the filter condition ([Overall Rank] - 1). Therefore, its rank remains the same.
Tables have an Overall Rank of 3. When the sub-category with an Overall Rank of 2 is removed (Chairs in this case), Tables move up in the overall ranking. However, since Tables are the top-ranked within the Furniture category, their Rank within the category would remain unchanged at 1.
Chairs have an Overall Rank of 2, which meets the filter condition and thus will be removed from the view. We cannot determine the change in Rank for Chairs because they are filtered out.
Accessories have an Overall Rank of 5. If any sub-category with a higher Overall Rank (1 to 4) is removed, the rank of Accessories within the Technology category could change because it is currently ranked 3 in its category. With the removal of Phones (Overall Rank 1), the Rank of Accessories could potentially increase.
Copiers have an Overall Rank of 6, which does not meet the filter condition. Therefore, its rank remains the same.
Machines have an Overall Rank of 4. If we remove Phones (Overall Rank 1), Machines will move up in the overall ranking and potentially within the Technology category as well, changing its Rank from 2 to 1.
Phones have an Overall Rank of 1, which does not meet the filter condition of being Overall Rank 2. Therefore, its rank remains the same.
Bookcases have an Overall Rank of 7, which does not meet the filter condition. Therefore, its rank remains the same.
Based on this analysis, when the sub-category with an Overall Rank of 2 (Chairs) is removed, the Rank value will change for Tables, Accessories, and Machines, as they will move up in the overall ranking within their respective categories. However, it's important to note that while Tables will move up in the overall ranking, their rank within the Furniture category would not change as they are already at the top. The rank changes for Accessories and Machines are due to the removal of Phones, which is ranked higher overall and within the Technology category.
You have a data source that has two tables named Table1 and Table2. Table1 is the primary table and Table2 is the secondary table.
You want to combine the tables by using Tableau Prep. The combined table must include only values from Table1 that do NOT match any values in Table2. The field values from Table2 must appear as null values.
Which type of join should you use?
To combine the tables by using Tableau Prep and include only values from Table1 that do not match any values in Table2, you should use a left only join. A left only join is a type of join that returns only the rows from the primary table (Table1) that have no matching rows in the secondary table (Table2). The field values from Table2 will appear as null values in the combined table. A left only join is also known as an anti-join or an exclusion join.Reference:
Tableau Certified Data Analyst Exam Prep Guide, page 9, section ''Preparing Data''
You want to create the following table in a view.
Which function should you use to create the Position column?
To create the Position column, you should use the FIRST () function. The FIRST () function returns the number of rows from the current row to the first row in the partition. For example, the first row in a partition will return 0, the second row will return -1, and so on. In this question, the partition is defined by the Segment field, and the table is sorted by the Sales field in descending order. Therefore, the Position column shows the offset of each row from the first row in each segment, based on the sales ranking.Reference:
Tableau Certified Data Analyst Exam Prep Guide, page 11, section ''Creating Calculated Fields''
You publish a dashboard that uses an extract. The extract refreshes every Monday at 10:00.
You need to ensure that the extract also refreshes on the last day of the month at 18:00.
What should you do?
To ensure that the extract also refreshes on the last day of the month at 18:00, you need to add a new extract refresh task to the workbook on Tableau Server. You can do this by selecting the workbook, selecting Refresh Extracts, and then choosing Schedule a Refresh. You can then select an existing schedule that matches your desired frequency and time, or create a new schedule with a custom frequency and time. This will add a new extract refresh task to the workbook, in addition to the existing one that refreshes every Monday at 10:00. You can also modify or delete the extract refresh tasks as needed.Reference:
Tableau Help: Refresh Data on a Schedule
Tableau Help: Manage Refresh Tasks
