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Tableau TCC-C01 - Tableau Certified Consultant Certification Exam

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Question #1 (Topic: demo questions)

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?

A.
ZN([Sales])*(1 - ZN([Discount]))
B.
CASE [Sector Parameter]
WHEN 1 THEN "green"
WHEN 2 THEN "yellow"
C.
SUM([Profit])/SUM([Sales])
D.
POWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
- 1
END
Correct Answer: C
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 precalculated
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.
Reference:
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.
Question #2 (Topic: demo questions)

A stakeholder has multiple files saved (CSV/Tables) in a single location. A few files from the location
are required for analysis. Data transformation (calculations)
is required for the files before designing the visuals. The files have the following attributes:
. All files have the same schema.
. Multiple files have something in common among their file names.
. Each file has a unique key column.
Which data transformation strategy should the consultant use to deliver the best optimized result?

A.
Use join option to combine/merge all the files together before doing the data transformation
(calculations).
B.
Use wildcard Union option to combine/merge all the files together before doing the data
transformation (calculations).
C.
Apply the data transformation (calculations) in each require file and do the wildcard union to
combine/merge before designing the visuals.
D.
Apply the data transformation (calculations) in each require file and do the join to combine/merge
before designing the visuals.
Correct Answer: B
Explanation:
Moving calculations to the data layer and materializing them in the extract can significantly improve
the performance of reports in Tableau. The calculation ZN([Sales])*(1 - ZN([Discount])) is a basic
calculation that can be easily computed in advance and stored in the extract, speeding up future
queries. This type of calculation is less complex than table calculations or LOD expressions, which are
better suited for dynamic analysis and may not benefit as much from materialization12.
Reference: The answer is based on the best practices for creating efficient calculations in Tableau, as
described in Tableau’s official documentation, which suggests using basic and aggregate calculations
to improve performance1. Additionally, the process of materializing calculations in extracts is
detailed in Tableau’s resources2.
Given that all files share the same schema and have a common element in their file names, the
wildcard union is an optimal approach to combine these files before performing any transformations.
This strategy offers the following advantages:
Efficient Data Combination: Wildcard union allows multiple files with a common naming scheme to
be combined into a single dataset in Tableau, streamlining the data preparation process.
Uniform Schema Handling: Since all files share the same schema, wildcard union ensures that the
combined dataset maintains consistency in data structure, making further data manipulation more
straightforward.
Pre-Transformation Combination: Combining the files before applying transformations is generally
more efficient as it reduces redundancy in transformation logic across multiple files. This means
transformations are written and processed once on the unified dataset, rather than repeatedly for
each individual file.
Reference:
Wildcard Union in Tableau: This feature simplifies the process of combining multiple similar files into
a single Tableau data source, ensuring a seamless and efficient approach to data integration and
preparation.
Question #3 (Topic: demo questions)

A client has many published data sources in Tableau Server. The data sources use the same databases
and tables. The client notices different departments
give different answers to the same business questions, and the departments cannot trust the data.
The client wants to know what causes data sources to return
different data.
Which tool should the client use to identify this issue?

A.
Tableau Prep Conductor
B.
Ask Data
C.
Tableau Catalog
D.
Tableau Resource Monitoring Tool
Correct Answer: C
Explanation:
The Tableau Catalog is part of the Tableau Data Management Add-on and is designed to help users
understand the data they are using within Tableau. It provides a comprehensive view of all the data
assets in Tableau Server or Tableau Online, including databases, tables, and fields. It can help identify
issues such as data quality, data lineage, and impact analysis. In this case, where different
departments are getting different answers to the same business questions, the Tableau Catalog can
be used to track down inconsistencies and ensure that everyone is working from the same, reliable
data source.
Reference: The recommendation for using Tableau Catalog is based on its features that support data
discovery, quality, and governance, which are essential for resolving data inconsistencies across
different departments12.
When different departments report different answers to the same business questions using the same
databases and tables, the issue often lies in how data is being accessed and interpreted differently
across departments. Tableau Catalog, a part of Tableau Data Management, can be used to solve this
problem:
Visibility: Tableau Catalog gives visibility into the data used in Tableau, showing users where data
Question #4 (Topic: demo questions)

Aconsultant creates a histogram that presents the distribution of profits across a client's customers.
The labels on the bars show percent shares. The consultant
used a quick table calculation to create the labels.
Now, the client wants to limit the view to the bins that have at least a 15% share. The consultant
creates a profit filter but it changes the percent labels.
Which approach should the consultant use to produce the desired result?

A.
Use a calculation with TOTAL() function instead of a quick table calculation.
B.
Add the [Profit] filter to the context.
C.
Filter with a table calculation WINDOW_AVG(MIN([Profit]), first(), last())
D.
Filter with the table calculation used to create labels.
Correct Answer: B
Explanation:
When a filter is applied directly to the view, it can affect the calculation of percentages in a
histogram because it changes the underlying data that the quick table calculation is based on. To
avoid this, adding the [Profit] filter to the context will maintain the original calculation of percent
shares while filtering out bins with less than a 15% share. This is because context filters are applied
before any other calculations, so the percent shares calculated will be based on the context-filtered
data, thus preserving the integrity of the original percent labels.
Reference: The solution is based on the principles of context filters and their order of operations in
Tableau, which are documented in Tableau’s official resources and community discussions123.
When a histogram is created showing the distribution of profits with labels indicating percent shares
using a quick table calculation, and a need arises to limit the view to bins with at least a 15% share,
applying a standard profit filter directly may undesirably alter how the percent labels calculate
because they depend on the overall distribution of data. Placing the [Profit] filter into the context
makes it a "context filter," which effectively changes how data is filtered in calculations:
Create a Context Filter: Right-click on the profit filter and select "Add to Context". This action changes
the order of operations in filtering, meaning the context filter is applied first.
Adjust the Percent Calculation: With the profit filter set in the context, it first reduces the data set to
only those profits that meet the filter criteria. Subsequently, any table calculations (like the percent
share labels) are computed based on this reduced data set.
View Update: The view now updates to display only those bins where the profits are at least 15%,
and the percent share labels recalculated to reflect the distribution of only the filtered (contextual)
data.
Reference:
Context Filters in Tableau: Context filters are used to filter the data passed down to other filters,
calculations, the marks card, and the view. By setting the profit filter as a context filter, it ensures that
calculations such as the percentage shares are based only on the filtered subset of the data.
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