QUESTION: 9 Exam Topic: 1, Knowledge-Based Section A

An executive-level workbook leverages 37 of the 103 fields included in a data source. Performance for the workbook is noticeably slower than other workbooks on the same Tableau Server.

What should the consultant do to improve performance of this workbook while following best practice?

Split some visualizations on the dashboard into many smaller visualizations on the same dashboard.
Connect to the data source via a custom SQL query.
Use filters, hide unused fields, and aggregate values.
OD. Restrict users from accessing the workbook to reduce server load.

Answer(s): C

Explanation:

To improve the performance of a Tableau workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.

Reference:

The best practices for optimizing workbook performance in Tableau are well-documented in Tableau's official resources, including the Tableau Help Guide and the Designing Efficient Workbooks whitepaper, which provide detailed recommendations on how to streamline workbooks for better performance.

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QUESTION: 10 Exam Topic: 1, Knowledge-Based Section A

A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:
Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}

The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher than the customer expects.

The client asks a consultant to rewrite the calculation so the result meets their expectation.

Which calculation should the consultant use?

{INCLUDE [Customer ID]: COUNTD([Order ID])}
{FIXED [Customer ID], [Region]: COUNTD([Order ID])}
{EXCLUDE [Customer ID]: COUNTD([Order ID])}
{FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}

Answer(s): B

Explanation:

The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client's expectations.

Reference:

The LOD expressions in Tableau allow for precise control over the level of detail at which calculations are performed, which is essential for accurate data analysis. The use of {FIXED} expressions to specify the granularity of the calculation is a common practice and is well- documented in Tableau's official resources.

The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:
FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues. Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions.

Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation.

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QUESTION: 11 Exam Topic: 1, Knowledge-Based Section A

A client builds a dashboard that presents current and long-term stock measures. Currently, the data is at a daily level. The data presents as a bar chart that presents monthly results over current and previous years. Some measures must present as monthly averages.

What should the consultant recommend to limit the data source for optimal performance?

Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
Move calculating averages to data layer and aggregate dates to monthly level.
Limit data to current and previous years as well as to the last day of each month to eliminate the need to use the averages.

Answer(s): B

Explanation:

For optimal performance, it is recommended to limit the data to what is necessary for analysis, which in this case would be the current and previous years. Moving the calculation of averages to the data layer and aggregating the dates to a monthly level will reduce the granularity of the data, thereby improving the performance of the dashboard. This approach aligns with best practices for optimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed.

Reference:

The recommendation is based on the guidelines provided in Tableau's official documentation on optimizing workbook performance, which includes tips on data management and aggregation for better performance.

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QUESTION: 12 Exam Topic: 1, Knowledge-Based Section A

A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the following levels of data access that they can be granted.

. Group 1: Viewers who cannot see any information on profitability . Group 2: Viewers who can see profit and profit margin . Group 3: Viewers who can see profit margin but not the value of profit

Which approach should the consultant use to provide the required level of access?

Use user filters to access data on profitability to all groups. Then, create a calculated field that allows visibility of profit value to Group 2 and use the calculation in the view in the report.
Specify in the row-level security (RLS) entitlement table individuals who can see profit, profit margin, or none of these. Then, use the table data to create user filters in the report.
Use user filters to allow only Groups 2 and 3 access to data on profitability. Then, create a calculated field that limits visibility of profit value to Group 2 and use the calculation in the view in the report.
Specify with user filters in each view individuals who can see profit, profit margin, or none of these.

Answer(s): C

Explanation:

The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with.

Reference:

The solution is based on the capabilities and permission rules that are part of Tableau Server's security model, as detailed in the official Tableau documentation. These resources provide guidance on how to set up user filters and calculated fields to manage data access levels effectively.

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