About this exam
TCC-C01 is the Tableau exam Tableau Certified Consultant.
Paid practice material for this exam is not available yet, but you can try its free sample questions.
- Exam code
- TCC-C01
- Vendor
- Tableau
- Practice questions
- Not available yet
- Free sample questions
- 4
- Question bank last updated
- Not available yet
Try 2 sample questions
Taken from the free sample set for TCC-C01.
Question 1
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?
- AOption A:
ZN([Sales])*(1 - ZN([Discount]))
- BOption B:
CASE [Sector Parameter]
WHEN 1 THEN "green"
WHEN 2 THEN "yellow"
- COption C:
SUM([Profit])/SUM([Sales])
- DOption D:
POWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
- 1
END
Show answer and explanation
Correct answer: C
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
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?
- AOption A:
Use join option to combine/merge all the files together before doing the data transformation
(calculations).
- BOption B:
Use wildcard Union option to combine/merge all the files together before doing the data
transformation (calculations).
- COption C:
Apply the data transformation (calculations) in each require file and do the wildcard union to
combine/merge before designing the visuals.
- DOption D:
Apply the data transformation (calculations) in each require file and do the join to combine/merge
before designing the visuals.
Show answer and explanation
Correct answer: B
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.
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