Salesforce Analytics-Con-301 Exam Practice Test Instant Access

Question 1

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.

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.

Question 2

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?

AZN([Sales])*(1 - ZN([Discount]))

BCASE [Sector Parameter]
WHEN 1 THEN 'green'
WHEN 2 THEN 'yellow'

CSUM([Profit])/SUM([Sales])

DPOWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
- 1
END

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 pre-calculated 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.

References:

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 3

A client is considering migrating from Tableau Server to Tableau Cloud.

Which two elements are determining factors of whether the client should use Tableau Server or Tableau Cloud? Choose two.

AWhether or not the client plans to leverage single sign-on (SSO)

BWhether or not there are large numbers of concurrent extract refreshes

CWhether or not the client needs the ability to connect to public, cloud-based data sources

DAmount of data storage used on the client's existing server

Answer : A, B

When considering a migration from Tableau Server to Tableau Cloud, two critical factors to consider are the client's need for single sign-on (SSO) and the volume of concurrent extract refreshes.

Single Sign-On (SSO): Tableau Cloud supports SSO, which can streamline user authentication and enhance security. If the client plans to leverage SSO, Tableau Cloud may be a suitable choice1.

Concurrent Extract Refreshes: The number of concurrent extract refreshes is a significant factor because it impacts performance and resource allocation. Tableau Server might be more appropriate if the client has a high volume of concurrent extract refreshes, as it allows for more control over the infrastructure to manage these workloads2.

References: The decision between Tableau Server and Tableau Cloud should be based on specific organizational needs, including security, compliance, performance, and scalability. The official Tableau resources provide guidance on these factors12. Additionally, discussions in the Tableau Community highlight the importance of considering these elements when choosing between Tableau Server and Tableau Cloud1.

Question 4

SIMULATION

From the desktop, open the CC workbook. Use the US Population Estimates data source.

You need to shape the data in US Population Estimates by using Tableau Desktop. The data must be formatted as shown in the following table.

Open the Population worksheet. Enter the total number of records contained in the data set into the Total Records parameter.

From the File menu in Tableau Desktop, click Save.

ASee the complete Steps below in Explanation

Answer : A

To shape the data in the 'US Population Estimates' data source and enter the total number of records into the 'Total Records' parameter in Tableau Desktop, follow these steps:

Open the CC Workbook and Access the Worksheet:

From the desktop, double-click on the CC workbook to open it in Tableau Desktop.

Navigate to the Population worksheet by selecting its tab at the bottom of the window.

Format and Shape the Data:

Ensure the data types match those specified in the requirements: Sex, Origin, Race as strings; Year, Age, Population as whole numbers.

To verify or change the data type, click on the dropdown arrow next to each field name in the Data pane and select 'Change Data Type' if necessary.

Calculate Total Number of Records:

Create a new calculated field named 'Total Records'. To do this, right-click in the Data pane and select 'Create Calculated Field'.

Enter the formula COUNT([Record ID]) or SUM([Number of Records]) depending on how the data source identifies each row uniquely.

Drag this new calculated field onto the worksheet to display the total number of records.

Enter the Value into the Total Records Parameter:

Locate the 'Total Records' parameter in the Data pane. Right-click on the parameter and select 'Edit'.

Manually enter the number displayed from the calculated field into the parameter, ensuring accuracy to meet the data shaping requirement.

Save Your Changes:

From the File menu, click 'Save' to ensure all your changes are stored.

References:

Tableau Desktop Guide: Provides detailed instructions on managing data types, creating calculated fields, and updating parameters.

Tableau Data Shaping Techniques: Outlines effective methods for manipulating and structuring data for analysis.

This process will ensure the data in the 'US Population Estimates' is accurately shaped according to the specified format and that the total number of records is correctly calculated and entered into the designated parameter. This thorough approach ensures data integrity and accuracy in reporting.

Question 5

A client wants to flag orders that have sales higher than the regional average.

Which calculated field will produce the required result?

[Sales]

{ FIXED [Order ID] : SUM([Sales]) }

B{ FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : SUM([Sales]) }

C{ FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }

D{ FIXED [Order ID] : SUM([Sales]) }
>
{ INCLUDE [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }

Answer : C

To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:

Correct Formula: { FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }

This calculation uses a Level of Detail (LOD) expression:

The left part of the formula { FIXED [Order ID] : SUM([Sales]) } calculates the total sales for each individual order.

The right part { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } calculates the average sales per order within each region.

The > operator is used to compare these two values to determine if the sales for each order exceed the regional average.

References This formula utilizes Tableau's LOD expressions to perform complex comparisons across different dimensions of the data, as explained in Tableau's official training materials on LOD calculations.

Question 6

A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the

following levels of data access that they can be granted.

. Group 1: Viewers who cannot see any information on profitability

. Group 2: Viewers who can see profit and profit margin

. Group 3: Viewers who can see profit margin but not the value of profit

Which approach should the consultant use to provide the required level of access?

AUse user filters to access data on profitability to all groups. Then, create a calculated field that allows visibility of profit value to Group 2 and
use the calculation in the view in the report.

BSpecify in the row-level security (RLS) entitlement table individuals who can see profit, profit margin, or none of these. Then, use the table
data to create user filters in the report.

CUse user filters to allow only Groups 2 and 3 access to data on profitability. Then, create a calculated field that limits visibility of profit value to
Group 2 and use the calculation in the view in the report.

DSpecify with user filters in each view individuals who can see profit, profit margin, or none of these.

Answer : C

The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with12.

References: The solution is based on the capabilities and permission rules that are part of Tableau Server's security model, as detailed in the official Tableau documentation12. These resources provide guidance on how to set up user filters and calculated fields to manage data access levels effectively.

Question 7

A client notices that while creating calculated fields, occasionally the new fields are created as strings, integers, or Booleans. The client asks a consultant if

there is a performance difference among these three data types.

What should the consultant tell the customer?

AStrings are fastest, followed by integers, and then Booleans.

BIntegers are fastest, followed by Booleans, and then strings.

CStrings, integers, and Booleans all perform the same.

DBooleans are fastest, followed by integers, and then strings.

Answer : B

In Tableau, the performance of calculated fields can vary based on the data type used. Calculations involving integers and Booleans are generally faster than those involving strings. This is because numerical operations are typically more efficient for a computer to process than string operations, which can be more complex and time-consuming. Therefore, when performance is a consideration, it is advisable to use integers or Booleans over strings whenever possible.

References: The performance hierarchy of data types in Tableau calculations is documented in resources that discuss best practices for optimizing Tableau performance1.

Salesforce Certified Tableau Consultant Analytics-Con-301 Exam Practice Test