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NEW QUESTION # 74
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?
Answer: B
Explanation:
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References: The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts. These practices suggest keeping the extract's data set short through filtering1. Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
Extract Filter: This involves setting filters that apply directly when the data is extracted from the source.
This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.
NEW QUESTION # 75
A business analyst is creating a view of the top 10 customers for each region. The analyst has set a "Top 10" filter on Customer Name. However, it did not display the top 10 customers per region, as shown in the image below.
Which type of filter should the business analyst add to filter for region?
Answer: A
Explanation:
The issue occurs because of Tableau's Order of Operations.
Key Tableau logic:
* Top N filters are a type of Dimension filter.
* Dimension filters are evaluated after Context filters.
* When you place Region on Filters (as a standard dimension filter), Tableau:
* First applies the Customer Name Top 10 filter across the entire data set, not per region.
* Then limits the view to the selected region(s).
* This results in seeing the global Top 10 customers, not the Top 10 per region.
How to fix it:
To force Tableau to compute Top 10 customers within each region, the Region filter must be applied before the Top N Customer filter.
This is done by making Region a Context Filter.
Effect of a Context Filter:
* Context filters are executed before the Top N filter.
* Region becomes the context.
* Tableau then evaluates the Top 10 customers inside each region's subset of data.
This produces the correct "Top 10 customers per region".
Why the other options are incorrect:
A). Extract filter
Applies once when creating the extract; does not control Top N logic inside the workbook.
B). Dimension filter
This is what the analyst already has - and it causes the unwanted behavior because it happens after the Top N filter.
C). Table Calculation filter
Top N is not a table calculation; table calc filters cannot fix this problem.
Only the Context Filter changes the execution order so Top N works per region.
* Tableau Order of Operations showing Context Filters applied before Top N filters.
* Best practices recommending Context Filters when Top N must be computed within subcategories.
* Filtering documentation explaining that Top N filters require context when additional dimensional filters are present.
NEW QUESTION # 76
A customer wants to leverage generative AI capabilities. The customer is currently on Tableau Server 2023.1.
How is the customer able to leverage generative AI in Tableau?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's official generative AI capability-Tableau Pulse and Einstein-powered Tableau AI features- are available only on Tableau Cloud, not Tableau Server.
Key Tableau facts:
* Tableau Server (any version, including new ones) does not provide generative AI capabilities.
* Tableau Cloud includes AI features such as:
* Tableau Pulse
* Einstein Copilot
* Natural language questions
* Automated insights
* Upgrading Tableau Server does not provide generative AI.
* Extensions and accelerators do not enable AI functionality.
Therefore, the customer must migrate from Tableau Server to Tableau Cloud to leverage generative AI.
* Tableau AI/Pulse documentation stating availability only in Tableau Cloud.
* Feature comparison charts showing generative AI unavailable on Tableau Server.
NEW QUESTION # 77
A Tableau consultant tasked with evaluating a data structure is handed the below sample dataset.
Which two statements are true about the dataset? Choose two.
Answer: B,C
Explanation:
The dataset shown is a classic "wide" format":
* A single row per state
* Separate columns for each year: 2019, 2020, 2021, 2022, 2023, 2024
Tableau's documentation on data structure and pivoting explains:
# Why A is TRUE
Tableau documentation identifies wide datasets (multiple columns representing categories such as years, months, or similar time periods) as high-maintenance structures because:
* For every new year, a new column must be added.
* Metadata and calculations must be updated each time.
* This type of structure is described as having poor scalability and higher maintenance.
This dataset fits that exact description, so A is correct.
# Why C is TRUE
According to Tableau's "Pivot Data from Columns to Rows" section:
* Wide datasets can and should often be pivoted so that repeated columns (such as year columns) become rows.
* Pivoting enables dynamic capabilities such as:
* Year filters (year selector)
* Time-series analysis
* Consistent aggregations
* Simplified calculations
Pivoting this dataset would produce:
State
Year
Value
Alabama
2019
2300.39
Alabama
2020
3030.39
...
...
...
This makes the dataset tall and tidy, which Tableau identifies as better for analysis and dashboard interactivity.
Therefore, C is correct.
# Why B is FALSE
The column names (2019, 2020, 2021...) are simply numbers.
Tableau documentation stresses that good metadata includes descriptive column names.
These column names:
* Do not indicate what the measure represents (Revenue? Sales? Population?)
* Only show the year, not the meaning of the metric
Thus they are not considered accurate or descriptive column names.
# Why D is FALSE
The dataset is already denormalized, not normalized.
Denormalized data means combining multiple attributes (like multiple years) into one table, which is exactly what this dataset already does.
Tableau documentation explains that wide data is already denormalized, and the recommended fix is pivoting, not further denormalization.
Therefore, D is incorrect.
NEW QUESTION # 78
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?
Answer: B
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 # 79
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