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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Sharing | 10% | - Manage user permissions and profiles - Configure row-level security - Implement team sharing and access controls - Apply org-wide sharing rules |
| Topic 2: Migration and Data Integration | 25% | - Handle data mapping and transformation rules - Validate data accuracy and completeness - Configure data connections and integrations - Execute data migration procedures - Design data model for Tableau CRM - Implement data transformations |
| Topic 3: Discovery and Requirements Gathering | 13% | - Define success criteria - Assess current state environment - Identify key stakeholder requirements - Document functional and technical requirements |
| Topic 4: Administration and Troubleshooting | 10% | - Monitor performance and optimization - Handle user support and training - Manage data refresh schedules - Troubleshoot common issues - Manage analytics environment settings |
| Topic 5: Implementation Planning | 15% | - Plan data integration strategy - Define migration approach - Create project timeline and milestones - Design solution architecture - Identify risks and mitigation strategies |
| Topic 6: Tableau CRM / Einstein Analytics Implementation | 27% | - Create and manage datasets - Build and customize lenses and dashboards - Build custom widgets and components - Implement Einstein Analytics features - Configure analytics applications and dashboards - Implement advanced analytics features - Configure data transforms using SAQL |
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NEW QUESTION # 39
During a Tableau Cloud implementation, a Tableau consultant has been tasked with implementing row-level security (RLS). They have already invested in implementing RLS within their own database for their legacy reporting solution. The client wants to know if they will be able to leverage their existing RLS after the Tableau Cloud implementation.
Which two requirements should the Tableau consultant share with the client? Choose two.
Answer: A,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Tableau Cloud, database-level RLS can be used only with live connections because:
* Tableau Cloud issues SQL queries using the logged-in user's identity.
* Extracts break RLS because data is pulled out of the database and stored in Tableau's hyper file.
* To leverage existing RLS rules, Tableau must query the database directly for the user.
Therefore:
Requirement 1:
The Tableau Cloud username (email) must exist in the database
so that the database can enforce RLS using the authenticated identity.
Requirement 2:
Only live data connections support database-native RLS.
Extracts bypass database security and therefore cannot use RLS defined in the database.
Option D is incorrect because RLS is enforced in the database, not configured in Tableau Cloud.
Option B is incorrect because extracts cannot use database RLS.
Thus, correct answers are A and C.
* Tableau Cloud live connection security requirements.
* Database RLS documentation requiring matching database user identities.
* Explanation that extracts bypass database permission systems.
NEW QUESTION # 40
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: A
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 # 41
For a new report, a consultant needs to build a data model with three different tables, including two that contain hierarchies of locations and products. The third table contains detailed warehousing data from all locations across six countries. The consultant uses Tableau Cloud and the size of the third table excludes using an extract.
What is the most performant approach to model the data for a live connection?
Answer: C
Explanation:
For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau's relationships feature, which optimizes queries for the underlying database.
References: The best practices for live connections in Tableau Cloud suggest using relationships to manage complex data models efficiently1. Additionally, Tableau's documentation on connecting data sources recommends using relationships for better performance with live connections2.
NEW QUESTION # 42
A multi-national company wants to have a Tableau dashboard that will provide country-level information for both its forecast summaries and year-on-year metrics. The company wants to toggle between these two views while leaving main key performance indicators (KPIs) visible on the main dashboard.
Which method is the most efficient in achieving the company's requirements?
Answer: A
Explanation:
. Add the calculated fields as a Detail under the Marks card of the floating view, create a "Change Parameter" action, and set the
"Target Parameter" and "Source Fields" to the parameter and calculated field you created.
. Check the box for "Control visibility using value" in the Layout tab of the floating view and select the parameter you created.
Explanation:
The most efficient method for toggling between two views (forecast summaries and year-on-year metrics) while keeping main KPIs visible involves using a parameter and calculated fields for controlling visibility:
Create a Boolean Parameter: This parameter will have two aliases representing the two views ("Forecast View" and "Year-on-Year View"). This allows the user to select which view they wish to see directly from the dashboard.
Calculated Field: Create a calculated field that always returns True. This field acts as a constant placeholder to enable the visibility control tied to the parameter.
Dashboard Setup: Place both the forecast summary and the year-on-year metrics sheets on the dashboard. Set the year-on-year metrics sheet as a floating object over the forecast summary.
Visibility Control: Use the "Control visibility using value" option in the Layout tab for the floating year-on-year metrics view. Tie this setting to the Boolean parameter so that changing the parameter will show or hide this view without affecting the main KPIs displayed on the dashboard.
Interactivity: Implement a "Change Parameter" dashboard action where selecting different options in the dashboard (e.g., clicking on certain parts) triggers the parameter to change, thus toggling the visible view.
References
This method leverages Tableau's dashboard interactivity features including parameters, calculated fields, and visibility settings, as recommended in Tableau's user guide on dynamic dashboard design.
NEW QUESTION # 43
A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that provides the best user experience across all the platforms.
Which approach should the consultant take to achieve these results?
Answer: A
Explanation:
For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:
Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.
Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices. Fixed sizes prevent components from resizing in ways that could disrupt the dashboard's readability or functionality.
Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.
References
This approach leverages Tableau's device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau's official guides on creating and managing device-specific dashboards.
NEW QUESTION # 44
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