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A client has a large data set that contains more than 10 million rows.
A consultant wants to calculate a profitability threshold as efficiently as possible. The calculation must classify the profits by using the following specifications:
. Classify profit margins above 50% as Highly Profitable.
. Classify profit margins between 0% and 50% as Profitable.
. Classify profit margins below 0% as Unprofitable.
Which calculation meets these requirements?
The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:
Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the '>=' operator to include exactly 50% as 'Highly Profitable'.
Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.
Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.
Logical Order in Conditional Statements: It is crucial in programming and data calculation to ensure that conditions in IF statements are structured in a logical and non-overlapping manner to accurately categorize all possible values.
A client has several long-term shipping contracts with different vendors that set rates based on shipping volume and speed. The client requests a dashboard
that allows them to model shipping costs for the next week based on the selected shipping vendor. Speed for the end user is critical.
Which dashboard building strategy will deliver the desired result?
For modeling shipping costs based on varying vendor contracts and ensuring speed in dashboard performance, the suggested approach involves:
Calculated Field with Parameter: Utilize a calculated field that dynamically references a user-selected parameter for the shipping vendor. This parameter adjusts the cost calculations based on selected vendor characteristics (like volume and speed).
Aggregate Results: After calculating individual shipping costs, aggregate these costs to provide a concise, summarized view of potential expenses for the upcoming week. This method ensures the dashboard remains performant by reducing the load of processing individual line items in real-time.
Why This Works: By using parameters and calculated fields, the dashboard can quickly adapt to user inputs without needing to re-query the entire dataset. Aggregating the results further improves performance and user experience by simplifying the output.
Reference This strategy leverages Tableau's capability to handle dynamic calculations with parameters and is recommended for scenarios where performance and user-driven interaction are priorities. Tableau's performance optimization resources and dashboard design guidelines detail these techniques.
A client calculates the percent of total sales for a particular region compared to all regions.
Which calculation will fix the automatic recalculation on the % of total field?
To correctly calculate the percent of total sales for a particular region compared to all regions, and to ensure that the calculation does not get inadvertently recalculated with each region filter application, the recommended calculation is:
{FIXED [Region]: sum([Sales])}: This part of the formula computes the sum of sales for each region, regardless of any filters applied to the view. It uses a Level of Detail expression to fix the sum of sales to each region, ensuring that filtering by regions won't affect the calculated value.
SUM([Sales]): This part computes the total sum of sales across all regions and is recalculated dynamically based on the filters applied to other parts of the dashboard or worksheet.
Combining the two parts: By dividing the fixed regional sales by the total sales, we get the proportion of sales for each region as compared to the total. This calculation ensures that while the denominator adjusts according to filters, the numerator remains fixed for each region, accurately reflecting the sales percentage without being affected by the region filter directly.
Reference This calculation follows Tableau's best practices for using Level of Detail expressions to manage computation granularity in the presence of dashboard filters, as outlined in the Tableau User Guide and official Tableau training materials.
A client wants to provide sales users with the ability to perform the following tasks:
* Access published visualizations and published data sources outside the company network.
* Edit existing visualizations.
* Create new visualizations based on published data sources.
. Minimize licensing costs.
Which site role should the client assign to the sales users?
The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.
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?
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.
