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Salesforce Certified Tableau Consultant Sample Questions (Q35-Q40):

NEW QUESTION # 35
A client needs to design row-level security (RLS) measures for their reports. The client does not currently have Tableau Data Management Add-on, and it may be an option in the future.
What should the consultant recommend as the safest and easiest way to manage for the long term?

A. Create User filters based on data policies and apply them to a published data source.
B. Create User filters based on data policies and apply them to views using set filters and option Server/Create User Filter.
C. Create User filters in each view of each report using set filters and option Server/Create User Filter.
D. Create User filters for each report using a table joined to its data source and using the option Apply to All Sheet Using the Data Source.

Answer: A

Explanation:
For implementing row-level security (RLS) without the Tableau Data Management Add-on, the best approach is to integrate user filters into the published data source:
Creating User Filters on Published Data Source: This method involves defining user filters that apply directly to the data source before it is published to the Tableau Server. This ensures that any workbook or view leveraging this data source inherently respects the row-level security settings.
To implement this, create a calculated field in Tableau that defines the security logic, typically using a formula that references user functions (like USERNAME() or ISMEMBEROF()). Drag this field to the Filters shelf and configure it to match the security rules (who can see what data).
Once configured, publish the data source to Tableau Server with these filters in place. This approach centralizes security management, making it easier to maintain and update security policies as they are applied universally to all workbooks using this data source.
This strategy is safe as it reduces the risk of accidental data exposure through individual workbook misconfiguration and simplifies long-term maintenance of security policies.
References
This method follows Tableau's best practices for implementing row-level security as detailed in Tableau's security management resources. It ensures robust, maintainable security measures that scale with organizational needs without requiring additional add-ons.

NEW QUESTION # 36
A client notices that while creating calculated fields, occasionally the new fields are created as strings, integers, or Booleans. The client asks a consultant if there is a performance difference among these three data types.
What should the consultant tell the customer?

A. Integers are fastest, followed by Booleans, and then strings.
B. Booleans are fastest, followed by integers, and then strings.
C. Strings are fastest, followed by integers, and then Booleans.
D. Strings, integers, and Booleans all perform the same.

Answer: A

Explanation:
In Tableau, the performance of calculated fields can vary based on the data type used. Calculations involving integers and Booleans are generally faster than those involving strings. This is because numerical operations are typically more efficient for a computer to process than string operations, which can be more complex and time-consuming. Therefore, when performance is a consideration, it is advisable to use integers or Booleans over strings whenever possible.
References: The performance hierarchy of data types in Tableau calculations is documented in resources that discuss best practices for optimizing Tableau performance1.

NEW QUESTION # 37

From the desktop, open the NYC
Property Transactions workbook.
You need to record the performance of
the Property Transactions dashboard in
the NYC Property Transactions.twbx
workbook. Ensure that you start the
recording as soon as you open the
workbook. Open the Property
Transactions dashboard, reset the filters
on the dashboard to show all values, and
stop the recording. Save the recording in
C:CCData.
Create a new worksheet in the
performance recording. In the worksheet,
create a bar chart to show the elapsed
time of each command name by
worksheet, to show how each sheet in
the Property Transactions dashboard
contributes to the overall load time.
From the File menu in Tableau Desktop,
click Save. Save the performance
recording in C:CCData.

Answer:

Explanation:
See the complete Steps below in Explanation:
Explanation:
To record the performance of the Property Transactions dashboard in the NYC Property Transactions.twbx workbook and analyze it using a bar chart, follow these detailed steps:
* Open the NYC Property Transactions Workbook:
* From the desktop, double-click the NYC Property Transactions.twbx workbook to open it in Tableau Desktop.
* Start Performance Recording:
* Before doing anything else, navigate to the 'Help' menu in Tableau Desktop.
* Select 'Settings and Performance', then choose 'Start Performance Recording'.
* Open the Property Transactions Dashboard and Reset Filters:
* Navigate to the Property Transactions dashboard within the workbook.
* Reset all filters to show all values. This usually involves selecting the dropdown on each filter and choosing 'All' or using a 'Reset' button if available.
* Stop the Performance Recording:
* Go back to the 'Help' menu.
* Choose 'Settings and Performance', then select 'Stop Performance Recording'.
* Tableau will automatically open a new tab displaying the performance recording results.
* Save the Performance Recording:
* In the performance recording results tab, go to the 'File' menu.
* Click 'Save As' and navigate to the C:CCData directory.
* Save the file, ensuring it is stored in the desired location.
* Create a New Worksheet for Performance Analysis:
* Return to the NYC Property Transactions workbook and create a new worksheet by clicking on the 'New Worksheet' icon.
* Drag the 'Command Name' field to the Columns shelf.
* Drag the 'Elapsed Time' field to the Rows shelf.
* Ensure that the 'Worksheet' field is also included in the analysis to break down the time by individual sheets within the dashboard.
* Choose 'Bar Chart' from the 'Show Me' options to display the data as a bar chart.
* Customize and Finalize the Bar Chart:
* Adjust the axes and labels to clearly display the information.
* Format the chart to enhance readability, applying color coding or sorting as needed to emphasize sheets with longer load times.
* Save Your Work:
* Once the new worksheet and the performance recording are complete, ensure all work is saved.
* Navigate to the 'File' menu and click 'Save', confirming that changes are stored in the workbook.
References:
Tableau Help Documentation: Provides guidance on how to start and stop performance recordings and analyze them.
Tableau Visualization Techniques: Offers tips on creating effective bar charts for performance data.
By following these steps, you have successfully recorded and analyzed the performance of the Property Transactions dashboard, providing valuable insights into how each component of the dashboard contributes to the overall load time. This analysis is crucial for optimizing dashboard performance and ensuring efficient data visualization.

NEW QUESTION # 38
A consultant has a view using a table calculation to calculate percent of total Sales by Category. The consultant would like to filter out particular categories, but wants the percent of total calculation to remain steady even as they filter items in or out.
What should the consultant do to achieve the desired impact?

A. Create a FIXED Level of Detail (LOD) expression, and then use that instead of the table calculation.
B. Filter Category by using a Context Filter instead of a Dimension Filter.
C. Filter Category by using a Data Source Filter instead of a Dimension Filter.
D. Create an aggregate expression, and then use that instead of the table calculation.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The key detail of the question:
"filter out particular categories, but wants the percent of total calculation to remain steady even as they filter items in or out." This means the percent of total must ignore filters.
Table calculations always operate after filters, except table calc filters like "Filter on Table Calculation," and after dimension filters, so filtering categories directly will change the denominator.
Tableau's documented solution for "percent of total that does not change with filtering" is:
# Use a FIXED LOD to define the stable denominator
A FIXED LOD expression "freezes" the aggregation level and is unaffected by dimension filters unless explicitly added to context.
This allows the consultant to compute:
{ FIXED : SUM([Sales]) }
or
{ FIXED [Category] : SUM([Sales]) }
Then percent of total becomes:
SUM([Sales]) / { FIXED : SUM([Sales]) }
The FIXED LOD stores the total before filters are applied, ensuring the percent remains steady.
This is exactly what Tableau documentation explains under:
* Level of Detail Expressions
* LODs and Order of Operations
* Using LODs to create filter-independent calculations
Thus, D is correct.
Why the other answers are wrong:
# A. Context Filter
Context filters run before FIXED LODs but after raw data.
If Category is put into context, LOD totals would be reduced.
Table calculation totals still change because table calcs run near the bottom of the pipeline.
# B. Data Source Filter
Data source filters remove rows before all table calculations and LODs.
This would make the percent of total incorrect, because filtered-out categories would physically be gone.
# C. Aggregate Expression
An aggregate field alone does not solve the issue because it still respects dimension filters.

NEW QUESTION # 39
Sales managers use a daily extract from Snowflake to see the previous day's snapshot.
Sales managers should only see statistics for their direct reports.
The company has Tableau Data Management on Tableau Cloud.
A consultant must design a centralized, low-maintenance RLS strategy.
What should the consultant implement?

A. Dynamic user filter
B. Data policy
C. Manual user filter
D. Built-in RLS security in Snowflake

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The key requirements:
* Tableau Cloud
* Extracts (not live data)
* Need RLS
* Want low maintenance
* Have Tableau Data Management
These requirements point directly to:
Data Policies (Virtual Connections RLS)
Tableau Data Management enables Virtual Connections and Data Policies, which provide:
* Centralized row-level security
* Integration with user identity via USERNAME()
* Reusable RLS logic across all downstream workbooks
* Works with extracts, unlike database RLS
* Minimal long-term maintenance
This is Tableau's recommended enterprise RLS method for Tableau Cloud with extracts.
Why the other options are incorrect:
A). Built-in RLS in Snowflake
Only works with live connections.
This client uses daily extracts, so database RLS is bypassed.
C). Manual user filter
High maintenance, must be edited manually per user - not scalable.
D). Dynamic user filter
Functional but must be recreated in each workbook, not centralized.
More maintenance than a data policy.
Therefore, Data Policy is the only low-maintenance, centralized RLS solution.
* Data Policies and Virtual Connections documentation describing centralized RLS
* Tableau Cloud & Extracts guidelines showing that database RLS cannot be reused
* RLS strategy best practices recommending Data Policies for scalable governance

NEW QUESTION # 40
......

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