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Salesforce Analytics-Con-301 Exam Syllabus Topics:

Topic
Details

Topic 1

Business Consulting: For Tableau Consultants, this section involves designing and troubleshooting calculations and workbooks to meet advanced analytical use cases. It covers selecting appropriate chart types, applying Tableau’s order of operations in calculations, building interactivity into dashboards, and optimizing workbook performance by resolving resource-intensive queries and other design-related issues.

Topic 2

Data Management: This part focuses on establishing governance and support for published content. Tableau Consultants are expected to manage data security, publish and maintain data sources and workbooks, and oversee content access. It includes applying governance best practices, using metadata APIs, and supporting administration functions to maintain data integrity and accessibility.

Topic 3

IT Management: This domain measures skills related to managing Tableau environments. It includes planning server upgrades, recommending deployment solutions (on-premise or cloud), and ensuring alignment between technical and business requirements for analytics infrastructure. It also involves troubleshooting and optimizing system performance relevant to Tableau Server and Cloud deployments.

Topic 4

Business Analysis: This section of the exam measures skills of Tableau Consultants focusing on evaluating the current state of analytics within an organization. It covers mapping business needs to Tableau capabilities, translating analytical requirements to best practices in Tableau, and recommending appropriate deployment options like Tableau Server or Tableau Cloud. It also includes evaluating existing data structures for supporting business needs and identifying performance risks and opportunities.

Topic 5

Data Visualization: This section evaluates the Tableau Consultant’s ability to design effective visual analytics solutions. It involves creating dashboards and visual reports that enhance user understanding, employing techniques like dynamic actions and advanced chart types, and ensuring performance optimization for an interactive user experience.

Salesforce Certified Tableau Consultant Sample Questions (Q64-Q69):

NEW QUESTION # 64
An online sales company has a table data source that contains Order Date. Products ship on the first day of each month for all orders from the previous month.
The consultant needs to know the average number of days that a customer must wait before a product is shipped.
Which calculation should the consultant use?

A. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
Calc2: AVG ([Order Date] - [Calc1])
B. Calc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date]))
Calc2: AVG([Order Date] - [Calc1])
C. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))
D. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))

Answer: D

Explanation:
The correct calculation to determine the average number of days a customer must wait before a product is shipped is to first find the shipping date, which is the first day of the following month after the order date.
This is done using DATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated using AVG(DATEDIFF('day',
[Order Date], [Calc1])). This approach ensures that the average wait time is calculated in days, which is the most precise measure for this scenario.
References: The solution is based on Tableau's date functions and their use in calculating differences between dates, which are well-documented in Tableau's official learning resources and consultant documents12.
To calculate the average waiting days from order placement to shipping, where shipping occurs on the first day of the following month:
Calculate Shipping Date (Calc1): Use the DATEADD function to add one month to the order date, then apply DATETRUNC to truncate this date to the first day of that month. This represents the shipping date for each order.
Calculate Average Wait Time (Calc2): Use DATEDIFF to calculate the difference in days between the original order date and the calculated shipping date (Calc1). Then, use AVG to average these differences across all orders, giving the average number of days customers wait before their products are shipped.
References:
Date Functions in Tableau: Functions like DATEADD, DATETRUNC, and DATEDIFF are used to manipulate and calculate differences between dates, crucial for creating metrics that depend on time intervals, such as customer wait times in this scenario.

NEW QUESTION # 65
A university has data on its undergraduate students and their majors by grade level (Freshman, Sophomore, Junior, Senior). The university is interested in visualizing the path students take as they change majors across grade levels.
Which visualization type should the consultant recommend?

A. Chord Chart
B. Radar Chart
C. Tree Chart
D. Sankey Diagram

Answer: D

Explanation:
To visualize the path students take as they change majors across different grade levels, a Sankey Diagram is highly effective. This type of visualization illustrates the flow and quantity between different stages or categories:
Sankey Diagram: It allows for a visual representation of students' movements between majors over time. Each flow's thickness is proportional to the number of students moving from one major to another, giving a clear, immediate visual cue of major popularity and student migration patterns.
To create a Sankey Diagram in Tableau, you typically need to prepare the data specifically for this type of chart. The data must include source (starting major), target (ending major), and the value (number of students). It often requires custom calculations and data reshaping to get the data in a format that a Sankey can use.
Once the data is prepared, you can use a combination of calculated fields, path binning, and line charts to simulate the flow effect in Tableau. External plugins or web-based integrations might also be employed for more direct implementations.
References
Sankey Diagrams are not natively supported in Tableau but can be implemented through creative use of data preparation and calculations, as suggested in advanced Tableau user communities and demonstrated in various Tableau public galleries.

NEW QUESTION # 66
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. SUM([Profit])/SUM([Sales])
B. ZN([Sales])*(1 - ZN([Discount]))
C. POWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))- 1END
D. CASE [Sector Parameter]
WHEN 1 THEN "green"
WHEN 2 THEN "yellow"

Answer: A

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 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.

NEW QUESTION # 67
A client wants to grant a user access to a data source hosted on Tableau Server so that the user can create new content in Tableau Desktop. However, the user should be restricted to seeing only a subset of approved data.
How should the client set up the filter before publishing the hyper file so that the Desktop user follows the same row-level security (RLS) as viewers of the end content?

A. Context Filter
B. Extract Filter
C. Data Source Filter
D. Apply Filter to All Using Related Data Sources

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's row-level security (RLS) is applied at the data source level so that all users who connect to the data source-whether through Tableau Desktop, Server, or Cloud-see only the data they are permitted to see.
According to Tableau documentation:
* A Data Source Filter is the correct method for enforcing consistent row-level security for all users.
* When a Data Source Filter is applied before publishing, it becomes part of the data source's metadata and is applied every time any user connects to the published source.
* This ensures that users creating new workbooks in Tableau Desktop are governed by the same RLS as users viewing published dashboards.
Context filters and extract filters do not provide secure RLS:
* A Context Filter only applies inside the workbook where it is created. It does not enforce security in Tableau Desktop when the data source is reused.
* An Extract Filter physically removes rows from the extract but does not enforce role-based filtering or dynamic RLS.
* "Apply Filter to All Using Related Data Sources" affects workbook behavior, not published data source security.
A Data Source Filter applied prior to publishing is Tableau's documented approach for secure, reusable row- level security.
* Row-Level Security implementation guidance describing Data Source Filters as the foundation of secure RLS.
* Tableau Server publishing workflow indicating that Data Source Filters travel with the published source.
* Documentation on why Context and Extract Filters do not enforce user-dependent row-level security.

NEW QUESTION # 68
A client currently has a workbook with the table shown below.

Which method will produce the output for the Total Sales Value field for all the categories shown in the table?

A. Quick Table Calculation
B. A Window Function
C. MAX() Function
D. Level of Detail (LOD) Calculation

Answer: D

Explanation:
To calculate the Total Sales Value for all categories as displayed in the table, an LOD expression is ideal. An LOD calculation in Tableau allows you to compute values at the data level that is different from the view level. In this case, since the Total Sales Value appears consistent across different sub-categories within each category, an LOD expression can be used to fix the Total Sales Value irrespective of the sub-category detail.
Here's how to set it up:
* Go to the Calculations area by right-clicking in the data pane and selecting "Create Calculated Field".
* Enter a name for the calculation, such as "Total Sales Value".
* Enter the LOD expression: { FIXED [Category] : SUM([Sales]) }. This calculation fixes the total sales to the category level, effectively summing sales for all sub-categories within each category, irrespective of how the data is broken down in the view.
* Drag this new calculated field into your visualization alongside the existing measures.
This method ensures that the Total Sales Value reflects the total for each category across all its sub-categories, matching the uniform values shown across different rows for each category in your table.
References
The explanation utilizes the concept of Level of Detail calculations in Tableau, which allows for advanced aggregations independent of the view level details. This concept is covered extensively in Tableau's official documentation and relevant training materials such as Tableau's online help resources.

NEW QUESTION # 69
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