Salesforce Analytics-Con-301 Exam - Topic 5 Question 8 Discussion

Actual exam question for Salesforce's Analytics-Con-301 exam

Question #: 8
Topic #: 5

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?

AUse join option to combine/merge all the files together before doing the data transformation (calculations).

BUse wildcard Union option to combine/merge all the files together before doing the data transformation (calculations).

CApply the data transformation (calculations) in each require file and do the wildcard union to combine/merge before designing the visuals.

DApply the data transformation (calculations) in each require file and do the join to combine/merge before designing the visuals.

Show Suggested Answer

Suggested 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.

References: 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.

References:

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.

by Jackie at Nov 21, 2025, 03:35 AM

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Jeffrey

5 days ago

C is good, but it could take longer with many files.

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Cherry

10 days ago

I lean towards option D. Joins are precise with unique keys.

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Bettina

15 days ago

Option A seems faster, but might miss some details.

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Alisha

20 days ago

I prefer option C. Transforming first keeps it clean.

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Tequila

25 days ago

I think option B is the best. Wildcard Union is efficient for multiple files.

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Kathrine

1 month ago

All files have the same schema, so A or B should work fine.

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Kirby

1 month ago

Definitely C! Transforming first makes more sense to avoid errors.

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Fernanda

2 months ago

Wait, can we really use wildcard unions for this? Sounds risky!

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Dianne

2 months ago

I disagree, A seems more straightforward for merging.

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Shawna

2 months ago

Haha, the consultant should just use a magic wand to make the data appear! No need for all this technical stuff.

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Herminia

2 months ago

Option D seems like the best choice. Joining the files after the transformations will give you more control over the data.

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Cherry

3 months ago

I'd go with option C. Applying the transformations first and then doing the union makes more sense to me.

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Elfrieda

3 months ago

I vaguely remember that merging files with a unique key might be better for analysis, so maybe option D could be the right choice?

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Luther

3 months ago

I think applying transformations to each file first makes sense, but I can't recall if we should join or union them afterward.

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Nickolas

3 months ago

I'm a bit unsure about whether to join or union the files. I feel like we did a similar question where joining was recommended, but I'm not completely confident.

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Katy

3 months ago

I remember we discussed the importance of schema consistency in our practice sessions, so I think using the wildcard union could be a good approach here.

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Juliana

3 months ago

This is a good example of where understanding data transformation strategies is crucial. I feel pretty confident I can tackle this one - I'll just need to double-check the details of each option to make the right choice.

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Helga

4 months ago

Option D seems like it could work too, but I'm not sure if that would be as efficient as option C. I think I'll go with C and see if I can justify that choice in my answer.

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Alishia

4 months ago

I'm a bit confused by the difference between the join and union options. I'll need to review those concepts to make sure I understand which one is more appropriate here.

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Almeta

4 months ago

Option B is the way to go! Wildcard union is the most efficient way to combine those files.

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Vernell

4 months ago

I think option B is the best choice here. Wildcard unions are super efficient!

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Adelaide

5 months ago

I'm with Shawna on this one. Wizardry is the way to go, forget about all these data transformation strategies.

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Frederic

5 months ago

Okay, let's see. The key seems to be that the files have the same schema and a common element in the file names. I'm leaning towards option C - that way I can apply the transformations to each file individually before combining them.

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