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| Section | Weight | Objectives |
|---|---|---|
| Plan and Prepare Data Connections | 22% | - Design data connections and transformations - Work with Web Data Connectors, Tableau Bridge - Implement row-level security and granularity strategies |
| Evaluate Current State and Recommend Solutions | 21% | - Recommend deployment options (Server / Cloud) - Map business requirements to Tableau capabilities - Assess data structure and performance risks |
| Design and Troubleshoot Calculations and Workbooks | 40% | - Select appropriate visualizations and chart types - Troubleshoot workbooks and dashboards - Build interactivity, optimize performance - Apply order of operations and advanced calculations |
| Establish Governance and Support Published Content | 16% | - Manage data sources, workbooks, and permissions - Apply governance and security strategies - Use metadata APIs and administrative views |
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NEW QUESTION # 81
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which calculation should the consultant use?
Answer: B
Explanation:
END
Explanation:
To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:
Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.
Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre-calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.
References:
Materialization in Extracts: This concept involves pre-calculating and storing complex aggregations or calculations within the Tableau data extract itself, improving performance by reducing the computational load during visualization rendering.
NEW QUESTION # 82
A client wants to produce a visualization to show quarterly profit growth and aggregated sales totals across a number of product categories from the data provided below.
Which set of charts should the consultant use to meet the client's requirements?
Answer: A
Explanation:
To effectively display quarterly profit growth and aggregated sales totals across different product categories, a combination of a Waterfall chart and a Tree Map is recommended:
* Waterfall Chart: This chart type is excellent for visualizing the sequential growth or decline of profits across different quarters for each sub-category. It clearly shows how profits accumulate over time, highlighting both positive and negative changes, which makes it ideal for tracking profit growth or decline through the quarters.
* Tree Map: A Tree Map can efficiently display aggregated sales totals where each block size represents the total sales of a product category, providing a quick, visually impactful comparison across categories. This is especially useful when the client wants to understand which categories contribute most to sales in a glanceable format.
Together, these charts provide a comprehensive overview of both profit trends over time (Waterfall Chart) and a comparative snapshot of sales performance across categories (Tree Map), meeting the client's need to analyze performance dynamics in a detailed yet consolidated manner.
References
These recommendations are based on common best practices for data visualization in Tableau, where specific chart types are chosen for their strengths in communicating certain types of data relationships and dynamics, as detailed in Tableau's official visualization guides.
NEW QUESTION # 83
A client is concerned that a dashboard has experienced degraded performance after they added additional quick filters. The client asks a consultant to improve performance.
Which two actions should the consultant take to fulfill the client's request? Choose two.
Answer: C,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Quick filters are one of the most expensive features in Tableau because they require queries to populate value lists and dynamic recalculations when filters change.
According to Tableau performance documentation:
1. Add an "Apply" Button
This prevents Tableau from re-running queries every time the user selects a filter value.
Queries are executed once when the user presses Apply.
This is a documented best practice for filter-heavy dashboards.
2. Replace Quick Filters with Filter Actions
Filter actions are far more efficient because:
* They leverage the existing view context
* They do not require separate filter UI queries
* They avoid the overhead of quick filter value lists
Tableau recommends using filter actions instead of multiple quick filters for better performance.
Why the other options are incorrect:
* B. Add filters to Context: Context filters make downstream filters faster, but do not reduce quick filter processing cost; they can even increase extract size and slow down the dashboard.
* C. Only Relevant Values: This actually slows performance because Tableau must re-evaluate the entire data set to determine relevancy every time filters update.
Thus, A and D are the correct performance-improvement approaches.
* Tableau Performance Checklist recommending Apply button for multi-select filters.
* Performance documentation advising the use of Filter Actions over multiple quick filters.
* Filtering best practices explaining the cost of Only Relevant Values.
NEW QUESTION # 84
Use the following login credentials to sign in
to the virtual machine:
Username: Admin
Password:
The following information is for technical
support purposes only:
Lab Instance: 40201223
To access Tableau Help, you can open the
Help.pdf file on the desktop.
From the desktop, open the CC workbook.
Open the Categorical Sales worksheet.
You need to use table calculations to
compute the following:
. For each category and year, calculate
the average sales by segment.
. Create another calculation to
compute the year-over-year
percentage change of the average
sales by category calculation. Replace
the original measure with the year-
over-year percentage change in the
crosstab.
From the File menu in Tableau Desktop, click
Save.
Answer:
Explanation:
See the complete Steps below in Explanation:
Explanation:
To compute the required calculations and update the worksheet in Tableau Desktop, follow these steps:
* Compute Average Sales by Segment for Each Category and Year:
* Open the CC workbook and navigate to the Categorical Sales worksheet.
* Drag the 'Sales' field to the Rows shelf if it's not already there.
* Drag the 'Segment' field to the Rows shelf as well, placing it next to 'Category' and 'Year'.
* Right-click on the 'Sales' field in the Rows shelf and select 'Quick Table Calculation' > 'Average'.
This will compute the average sales for each segment within each category and year.
* Create a Calculation for Year-over-Year Percentage Change:
* Right-click in the data pane and select 'Create Calculated Field'.
* Name the calculated field something descriptive, e.g., "YoY Sales Change".
* Enter the formula to calculate the year-over-year percentage change:
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1))
* Click 'OK' to save the calculated field.
* Replace the Original Measure with the Year-over-Year Percentage Change in the Crosstab:
* Remove the original 'Sales' measure from the view by dragging it off the Rows shelf.
* Drag the newly created "YoY Sales Change" calculated field to the Rows shelf where the 'Sales' field was originally.
* Format the "YoY Sales Change" field to display as a percentage. Right-click on the field in the Rows shelf, select 'Format', and adjust the number format to percentage.
* Save Your Changes:
* From the File menu, click 'Save' to ensure all your changes are stored.
References:
Tableau Help: Offers guidance on creating calculated fields and using table calculations.
Tableau Desktop User Guide: Provides instructions on formatting and saving worksheets.
These steps allow you to manipulate data within Tableau effectively, using table calculations to analyze trends and changes in sales data by category and segment over years.
NEW QUESTION # 85
A client wants to count all the distinct orders placed in 2010. They have written the following calculation, but the result is incorrect.
IF YEAR([Date])=2010 THEN COUNTD ([OrderID]) END
Which calculation will produce the correct result?
Answer: C
Explanation:
The correct calculation to count all distinct orders placed in 2010 involves placing the conditional inside the aggregation function, not the other way around. Here's how to correct the client's calculation:
Original Calculation Issue: The client's original calculation attempts to apply the COUNTD function within an IF statement, which does not work as expected because the COUNTD function cannot conditionally count within the scope of the IF statement.
Correct Calculation: COUNTD(IF YEAR([Date]) = 2010 THEN [OrderID] END). This calculation checks each order date; if the year is 2010, it returns the OrderID. The COUNTD function then counts all unique OrderIDs that meet this condition.
Why It Works: This method ensures that each order is first checked for the year condition before being counted, effectively filtering and counting in one step. It efficiently processes the data by focusing the distinct count operation only on relevant records.
References
This approach is consistent with Tableau's guidance on using conditional logic inside aggregation functions for accurate and efficient data calculations, as detailed in the Tableau User Guide under "Aggregations and Calculations".
NEW QUESTION # 86
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