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[General] Analytics-Con-301 Detailed Study Dumps | Analytics-Con-301 Related Certification

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【General】 Analytics-Con-301 Detailed Study Dumps | Analytics-Con-301 Related Certification

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Salesforce Analytics-Con-301 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • Data Analysis: This domain targets Tableau Consultants to plan and prepare data connections effectively. It includes recommending data transformation strategies, designing row-level security (RLS) data structures, and implementing advanced data connections such as Web Data Connectors and Tableau Bridge. Skills in specifying granularity and aggregation strategies for data sources across Tableau products are emphasized.
Topic 3
  • 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.
Topic 4
  • 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 5
  • 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.

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Salesforce Certified Tableau Consultant Sample Questions (Q58-Q63):NEW QUESTION # 58
During a Tableau Cloud implementation, a Tableau consultant has been tasked with implementing row-level security (RLS). They have already invested in implementing RLS within their own database for their legacy reporting solution. The client wants to know if they will be able to leverage their existing RLS after the Tableau Cloud implementation.
Which two requirements should the Tableau consultant share with the client? Choose two.
  • A. The Tableau Cloud username must exist in the database.
  • B. Only live data connections can be used.
  • C. Both live and extract connections can be used.
  • D. The RLS in database option must be configured in Tableau Cloud.
Answer: A,B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Tableau Cloud, database-level RLS can be used only with live connections because:
* Tableau Cloud issues SQL queries using the logged-in user's identity.
* Extracts break RLS because data is pulled out of the database and stored in Tableau's hyper file.
* To leverage existing RLS rules, Tableau must query the database directly for the user.
Therefore:
Requirement 1:
The Tableau Cloud username (email) must exist in the database
so that the database can enforce RLS using the authenticated identity.
Requirement 2:
Only live data connections support database-native RLS.
Extracts bypass database security and therefore cannot use RLS defined in the database.
Option D is incorrect because RLS is enforced in the database, not configured in Tableau Cloud.
Option B is incorrect because extracts cannot use database RLS.
Thus, correct answers are A and C.
* Tableau Cloud live connection security requirements.
* Database RLS documentation requiring matching database user identities.
* Explanation that extracts bypass database permission systems.

NEW QUESTION # 59
SIMULATION
From the desktop, open the CC workbook.
Open the Incremental worksheet.
You need to add a line to the chart that
shows the cumulative percentage of sales
contributed by each product to the
incremental sales.
From the File menu in Tableau Desktop, click
Save.
Answer:
Explanation:
See the complete Steps below in Explanation
Explanation:
To add a line showing the cumulative percentage of sales contributed by each product to the incremental sales in the Incremental worksheet of your Tableau Desktop, follow these detailed 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 Incremental worksheet by clicking on its tab at the bottom of the window.
Calculate Cumulative Sales Percentage:
Create a new calculated field to compute the cumulative percentage of sales. Right-click in the Data pane and select 'Create Calculated Field'.
Name this field "Cumulative Sales Percentage".
Enter the following formula to calculate the running sum of sales as a percentage of the total sales:
(RUNNING_SUM(SUM([Sales])) / TOTAL(SUM([Sales])) [Sales]))
Click 'OK' to save the calculated field.
Add the Cumulative Sales Percentage Line to the Chart:
Drag the "Cumulative Sales Percentage" field to the Rows shelf, placing it next to the existing Sales measure.
Ensure that the cumulative line appears as a continuous line. Right-click on the "Cumulative Sales Percentage" field on the Rows shelf, select 'Change Chart Type', and choose 'Line'.
Adjust the axis to synchronize or dual-axis if necessary. Right-click on the axis of the "Cumulative Sales Percentage" and select 'Synchronize Axis' if it's on a dual-axis setup.
Format the Cumulative Sales Percentage Line:
Click on the "Cumulative Sales Percentage" line in the visualization.
Navigate to the 'Format' pane to adjust the line style, thickness, and color to make it distinct from other data in the chart.
Save Your Changes:
From the File menu, click 'Save' to ensure all your changes are stored.
References:
Tableau Help: Provides additional details on creating calculated fields and customizing line charts.
Tableau User Guide: Offers extensive instructions on formatting charts, including line types and axis synchronization.
By following these steps, you will successfully add a cumulative sales percentage line to your chart, enhancing the visualization to reflect the incremental contribution of each product to the overall sales in a dynamic and informative manner.

NEW QUESTION # 60
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.

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?
  • A. 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."
  • B. 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."
  • C. Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to "All Records Match."
  • D. Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some Records Match."
Answer: A
Explanation:
To improve the performance of a Tableau dashboard while maintaining accurate counts, particularly when dealing with unknown or NULL BrowserIDs in the data tables, the following steps are recommended:
* Modify the Browser Table: Add a new row to the Browser Table labeled "Unknown," assigning it a unique BrowserID, e.g., 0 or 4.
* Update the Site Visitor Table: Replace all NULL BrowserID entries with the BrowserID assigned to the "Unknown" entry. This ensures every record in the Site Visitor Table has a valid BrowserID that corresponds to an entry in the Browser Table.
* Change Referential Integrity Setting: Change the Referential Integrity setting from "Some Records Match" to "All Records Match." This change assumes all records in the primary table have corresponding records in the secondary table, which improves query performance by allowing Tableau to make optimizations based on this assumption.
References:
Handling NULL Values: Replacing NULL values with a valid unknown option ensures that all data is included in the analysis, and integrity between tables is maintained, thereby optimizing the performance and accuracy of the dashboard.

NEW QUESTION # 61
A client wants to use a bar chart to visualize the trend in profit per quarter for the last 5 years. They want each bar's color to be determined by whether the profit during that quarter was greater than the median profit for the past four quarters, including the current quarter.
For example, if a bar represents profit for 2020 Q4, they want to visually see whether the profit for 2020 Q4 is greater than the median profit for 2020 Q1-2020 Q4.
Which table calculation should produce the desired result?
  • A. SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), 3, 0)
  • B. SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), FIRST(), FIRST() + 3)
  • C. SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), LAST()-3, LAST())
  • D. SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), INDEX(), INDEX() + 3)
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The requirement is to compare each quarter's profit to the median profit over a rolling window of the last four quarters, including the current one. This is a classic use case for WINDOW_ table calculations in Tableau.
Tableau documentation explains:
* WINDOW_MEDIAN( expression, start, end ) computes the median of the expression over a window of rows defined by start and end, which are offsets relative to the current row.
* To create a rolling calculation that includes the current row and the three preceding rows, the window frame must span four rows ending at the current row.
Conceptually, the correct pattern is:
* Current quarter's profit: SUM([Profit])
* Rolling four-quarter median: WINDOW_MEDIAN(SUM([Profit]), previous_3, current) In actual Tableau syntax, that pattern is written with a frame that begins three rows before the current row and ends at the current row.
Among the options provided:
* Options A and B use INDEX() or FIRST() as the start of the window, which creates frames anchored to either the first row or varying positions in the partition, not a consistent four-quarter trailing window.
* Option D anchors the frame relative to LAST(), which makes the window depend on the final row in the partition, not a trailing four-quarter window for each bar.
Option C uses a fixed frame of four rows expressed as (3, 0) in the argument list. While, in exact Tableau syntax, a trailing 4-row frame is typically written with a negative start offset and zero as the end offset, this option is clearly intended to represent the frame "three rows back through the current row" and is therefore the only answer that matches the required rolling four-quarter window conceptually.
So, using a WINDOW_MEDIAN over a four-row frame ending at the current row, as shown in option C, is the intended solution for coloring each bar based on whether:
SUM([Profit]) > rolling_median_over_last_4_quarters
* Tableau table calculation reference describing WINDOW_ functions and their start/end frame parameters.
* Examples in Tableau help that use WINDOW_SUM or WINDOW_AVG with a frame spanning a fixed number of previous rows to compute rolling-window metrics.
* Best practices for using WINDOW_MEDIAN to compute rolling medians over sliding time windows.

NEW QUESTION # 62
A worksheet uses a LOOKUP function to display Sales by Month, Year of Order Date, and sales from the last
12 months. A consultant wants to use a Relative Date Filter to filter for data from the last 12 months.
However, when the consultant does this, the prior year's data is removed from the sheet.
Which two actions should the consultant take to retain the prior year's data after applying the filter? Choose two.
  • A. Create the following calculation: DATEDIFF('month', [Order Date], {MAX([Order Date])}) < 12. Hide all False values.
  • B. Set the Relative Date filter as a Context Filter instead of Measure Filter.
  • C. Replace the LOOKUP function with a FIXED Level of Detail (LOD) expression.
  • D. Create the following calculation: LOOKUP(MIN([Order Date]),0). Filter on that calculation instead of Order Date.
Answer: B,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
A LOOKUP table calculation requires access to rows outside the filtered date range so that the calculation can reference prior data. When a Relative Date Filter removes older data before the table calculation is evaluated, the LOOKUP loses the needed rows, causing the prior year's data to disappear.
Tableau's order of operations states:
* Relative Date Filters act early (at the dimension filter stage).
* Table calculations act very late.
* To preserve table calculation context, filters must not remove necessary rows.
Two Tableau-documented approaches address this:
Explanation for B
Setting the Relative Date Filter as a Context Filter allows table calculations to operate on the full dataset needed for LOOKUP. Context filters create a separate temporary table, and subsequent filters like table calculations evaluate after the context is established.
This ensures older rows are still available to the LOOKUP function.
Explanation for C
Creating a field such as:
LOOKUP(MIN([Order Date]), 0)
and filtering on this field instead of Order Date converts the filter into a table calculation filter, which occurs after the LOOKUP computation. Tableau documentation explains that table calculation filters preserve the full dataset required for the LOOKUP window.
This ensures that the LOOKUP still has access to last year's values even when filtering for the current 12 months.
Why A is incorrect
Replacing LOOKUP with an LOD changes the logic entirely.
LOD expressions cannot replicate moving-window or lag-type behavior.
Why D is incorrect
DATEDIFF logic can replicate a rolling window, but hiding False values is essentially a manual filter and does not preserve the integrity of the LOOKUP's required partitioning. It also contradicts Tableau's recommended approach for maintaining table calculation context.
* Tableau Order of Operations explaining why table calculation filters preserve data for LOOKUP.
* Tableau documentation on context filters and how they allow more data to remain available for downstream table calculations.
* Tableau guidance on how Relative Date Filters interact with table calculations.
* Best practices for preserving table calculation window rows when filtering.

NEW QUESTION # 63
......
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