Salesforce Analytics-Con-301 Exam Questions Free Practice Test

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Questions # 21:

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?

Options:

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

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

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

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

Expert Solution

Answer

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.

[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., , ]

Questions # 22:

A consultant 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?

Options:

Use a calculation with TOTAL() function instead of a quick table calculation.

Add the [Profit] filter to the context.

Filter with a table calculation WINDOW_AVG(MIN([Profit]), first(), last())

Filter with the table calculation used to create labels.

Expert Solution

Answer

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.

[References: 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., References:, 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., , ]

Questions # 23:

A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:

Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}

The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher

than the customer expects.

The client asks a consultant to rewrite the calculation so the result meets their expectation.

Which calculation should the consultant use?

Options:

{INCLUDE [Customer ID]: COUNTD([Order ID])}

{FIXED [Customer ID], [Region]: COUNTD([Order ID])}

{EXCLUDE [Customer ID]: COUNTD([Order ID])}

{FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}

Expert Solution

Answer

Explanation

The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client’s expectations.

[References: The LOD expressions in Tableau allow for precise control over the level of detail at which calculations are performed, which is essential for accurate data analysis. The use of {FIXED} expressions to specify the granularity of the calculation is a common practice and is well-documented in Tableau’s official resources12., , The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:, FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues., Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions., References:, Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation., , ]

Questions # 24:

A client collects information about a web browser customers use to access their website. They then visualize the breakdown of web traffic by browser version.

The data is stored in the format shown below in the related table, with a NULL BrowserID stored in the Site Visitor Table if an unknown browser version

accesses their website.

Question # 24

The client uses "Some Records Match" for the Referential Integrity setting because a match is not guaranteed. The client wants to improve the performance of

the dashboard while also getting an accurate count of site visitors.

Which modifications to the data tables and join should the consultant recommend?

Options:

Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some Records Match."

Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and change the Referential Integrity to "All

Records Match."

Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and leave the Referential Integrity set to

"Some Records Match."

Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to "All Records Match."

Expert Solution

Questions # 25:

A client wants to report Saturday and Sunday regardless of the workbook's data source's locale settings.

Which calculation should the consultant recommend?

Options:

DATEPART('weekday', [Order Date])>=6

DATEPART('iso-weekday', [Order Date])>=6

DATENAME('iso-weekday', [Order Date])>=6

DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7

Expert Solution

Questions # 26:

An executive-level workbook leverages 37 of the 103 fields included in a data source. Performance for the workbook is noticeably slower than other workbooks

on the same Tableau Server.

What should the consultant do to improve performance of this workbook while following best practice?

Options:

Split some visualizations on the dashboard into many smaller visualizations on the same dashboard.

Connect to the data source via a custom SQL query.

Use filters, hide unused fields, and aggregate values.

OD. Restrict users from accessing the workbook to reduce server load.

Expert Solution

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