[May 07, 2026] Salesforce Analytics-Con-301 Real Exam Questions and Answers FREE
Pass Salesforce Analytics-Con-301 Exam Info and Free Practice Test
NEW QUESTION # 54
An analyst needs to interactively set a reference date to drive table calculations without leaving a view.
Which action should the analyst use?
- A. Highlight action
- B. Filter action
- C. Parameter action
- D. Running action
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's documentation on Parameter Actions states that they allow users to interactively update a parameter directly from the view-without opening the parameter control box.
To "interactively set a reference date" that drives a table calculation:
* A parameter must hold that reference date.
* A parameter action allows clicking or selecting a mark in the view to update the parameter value.
* Table calculations can then reference that parameter to change their computation dynamically.
Filter actions modify which data is shown, not a reference date.
Running actions change sheets, not computation parameters.
Highlight actions visually accent marks but do not set values.
Thus, parameter actions are the only mechanism that meets the requirement.
* Parameter Actions overview describing interactive parameter updates.
* Use cases where parameter actions drive table calculations.
* Action type comparison showing that only parameter actions update a computation value.
NEW QUESTION # 55
A client has a database that stores widget inventory by day and it is updated on a nonstandard schedule as shown below.
They want a data visualization that shows widget inventory daily, however their business unit does not have the ability to modify the data warehouse structure.
What should the client do to achieve the desired result?
- A. Create a temporary table in the database.
- B. Update the Widget Inventory Table to be a daily snapshot.
- C. Use Tableau Prep to add new rows.
- D. Use Tableau Desktop to visualize null values.
Answer: C
Explanation:
For a client who needs a daily visualization of widget inventory but cannot modify the data warehouse structure, the best approach is to use Tableau Prep to add new rows. Tableau Prep can be used to manipulate the existing dataset by adding missing date entries and appropriately adjusting inventory counts based on available data. This allows the creation of a complete daily snapshot for visualization without needing changes to the underlying database structure.
NEW QUESTION # 56
A client has a database that stores widget inventory by day and it is updated on a nonstandard schedule as shown below.
They want a data visualization that shows widget inventory daily, however their business unit does not have the ability to modify the data warehouse structure.
What should the client do to achieve the desired result?
- A. Create a temporary table in the database.
- B. Update the Widget Inventory Table to be a daily snapshot.
- C. Use Tableau Prep to add new rows.
- D. Use Tableau Desktop to visualize null values.
Answer: C
Explanation:
For a client who needs a daily visualization of widget inventory but cannot modify the data warehouse structure, the best approach is to use Tableau Prep to add new rows. Tableau Prep can be used to manipulate the existing dataset by adding missing date entries and appropriately adjusting inventory counts based on available data. This allows the creation of a complete daily snapshot for visualization without needing changes to the underlying database structure.
NEW QUESTION # 57
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. Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some 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. 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."
Answer: D
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 # 58
A client wants to count all the distinct orders placed in 2010. They have written the following calculation, but the result is incorrect.
IF YEAR([Date])=2010 THEN COUNTD ([OrderID]) END
Which calculation will produce the correct result?
- A. IF YEAR([Date])=2010 THEN {COUNTD ([OrderID])} END
- B. IF MIN(YEAR([Date]))=2010 THEN WINDOW_COUNTD([OrderID]) END
- C. COUNT(IF YEAR([Date])=2010 THEN [OrderID] END)
- D. COUNTD(IF YEAR([Date])=2010 THEN [OrderID] END)
Answer: D
Explanation:
The correct calculation to count all distinct orders placed in 2010 involves placing the conditional inside the aggregation function, not the other way around. Here's how to correct the client's calculation:
Original Calculation Issue: The client's original calculation attempts to apply the COUNTD function within an IF statement, which does not work as expected because the COUNTD function cannot conditionally count within the scope of the IF statement.
Correct Calculation: COUNTD(IF YEAR([Date]) = 2010 THEN [OrderID] END). This calculation checks each order date; if the year is 2010, it returns the OrderID. The COUNTD function then counts all unique OrderIDs that meet this condition.
Why It Works: This method ensures that each order is first checked for the year condition before being counted, effectively filtering and counting in one step. It efficiently processes the data by focusing the distinct count operation only on relevant records.
References
This approach is consistent with Tableau's guidance on using conditional logic inside aggregation functions for accurate and efficient data calculations, as detailed in the Tableau User Guide under "Aggregations and Calculations".
NEW QUESTION # 59
A client has many published data sources in Tableau Server. The data sources use the same databases and tables. The client notices different departments give different answers to the same business questions, and the departments cannot trust the data. The client wants to know what causes data sources to return different data.
Which tool should the client use to identify this issue?
- A. Ask Data
- B. Tableau Resource Monitoring Tool
- C. Tableau Prep Conductor
- D. Tableau Catalog
Answer: D
Explanation:
The Tableau Catalog is part of the Tableau Data Management Add-on and is designed to help users understand the data they are using within Tableau. It provides a comprehensive view of all the data assets in Tableau Server or Tableau Online, including databases, tables, and fields. It can help identify issues such as data quality, data lineage, and impact analysis. In this case, where different departments are getting different answers to the same business questions, the Tableau Catalog can be used to track down inconsistencies and ensure that everyone is working from the same, reliable data source.
References: The recommendation for using Tableau Catalog is based on its features that support data discovery, quality, and governance, which are essential for resolving data inconsistencies across different departments12.
When different departments report different answers to the same business questions using the same databases and tables, the issue often lies in how data is being accessed and interpreted differently across departments. Tableau Catalog, a part of Tableau Data Management, can be used to solve this problem:
Visibility: Tableau Catalog gives visibility into the data used in Tableau, showing users where data comes from, where it's used, and who's using it.
Consistency and Trust: It helps ensure consistency and trust in data by providing detailed metadata management that can highlight discrepancies in data usage or interpretation.
Usage Metrics and Lineage: It offers tools for tracking usage metrics and understanding data lineage, which can help in identifying why different departments might see different results from the same underlying data.
References:
Tableau Catalog Usage: The Catalog is instrumental in providing a detailed view of the data environment, allowing organizations to audit, track, and understand data discrepancies across different users and departments.
NEW QUESTION # 60
A client has a large data set that contains more than 10 million rows.
A consultant wants to calculate a profitability threshold as efficiently as possible. The calculation must classify the profits by using the following specifications:
. Classify profit margins above 50% as Highly Profitable.
. Classify profit margins between 0% and 50% as Profitable.
. Classify profit margins below 0% as Unprofitable.
Which calculation meets these requirements?
- A. IF [ProfitMargin]>=0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END - B. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSEIF [ProfitMargin] <0 Then 'Unprofitable'
END - C. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END - D. IF([ProfitMargin]>=0.50,'Highly Profitable', 'Profitable')
ELSE 'Unprofitable'
END
Answer: A
Explanation:
The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:
Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the ">=" operator to include exactly 50% as "Highly Profitable".
Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.
Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.
References:
Logical Order in Conditional Statements: It is crucial in programming and data calculation to ensure that conditions in IF statements are structured in a logical and non-overlapping manner to accurately categorize all possible values.
NEW QUESTION # 61
A client has a dashboard that uses a bar chart to visualize sales by Sub-Category and a detail table that has all the orders for the products within Sub- Category. The table has more than 10,000 rows of data and is slow to load.
A consultant plans to add an action so when the client interacts with the bar chart, only the relevant data appears in the table.
What will provide the fastest rendering of the dashboard?
- A. Add a highlight action and set Target Highlighting to All Fields.
- B. Add a highlight action and set Target Highlighting to Sub-Category.
- C. Add a filter action, set "Run action on" to Menu, and set "Clearing the selection will" to Show all values.
- D. Add a filter action, set "Run action on" to Select, and set "Clearing the selection will" to Exclude all values.
Answer: D
Explanation:
To optimize the dashboard rendering, particularly when dealing with a large dataset, a filter action is the most effective tool. Here's why the specified choice is optimal:
Add a filter action: This action creates a direct filter on the detail table based on the selection in the bar chart. It ensures that only data related to the selected sub-category is loaded into the table, significantly reducing load time and improving performance.
Set "Run action on" to Select: This setting means the filter action will be triggered as soon as the user selects a bar in the bar chart. Immediate activation of the filter ensures that the dashboard is interactive and responsive.
Set "Clearing the selection will" to Exclude all values: When the selection is cleared, this setting ensures that no data is shown, which avoids loading the entire dataset unnecessarily. This maintains performance when no sub-category is actively selected.
References
This strategy follows Tableau's performance best practices by using actions to limit the amount of data processed and rendered, as detailed in the Tableau User Guide and training materials on Dashboard Actions for optimizing large datasets.
NEW QUESTION # 62
A client calculates the percent of total sales for a particular region compared to all regions.
Which calculation will fix the automatic recalculation on the % of total field?
- A. {FIXED [Region]:sum([Sales])}/{FIXED :SUM([Sales])
- B. {FIXED [Region]:[Sales]}/{FIXED: SUM([Sales])}
- C. {FIXED [Region]:sum([Sales])}
- D. {FIXED [Region]:sum([Sales])}/SUM([Sales]}
Answer: D
Explanation:
To correctly calculate the percent of total sales for a particular region compared to all regions, and to ensure that the calculation does not get inadvertently recalculated with each region filter application, the recommended calculation is:
{FIXED [Region]: sum([Sales])}: This part of the formula computes the sum of sales for each region, regardless of any filters applied to the view. It uses a Level of Detail expression to fix the sum of sales to each region, ensuring that filtering by regions won't affect the calculated value.
SUM([Sales]): This part computes the total sum of sales across all regions and is recalculated dynamically based on the filters applied to other parts of the dashboard or worksheet.
Combining the two parts: By dividing the fixed regional sales by the total sales, we get the proportion of sales for each region as compared to the total. This calculation ensures that while the denominator adjusts according to filters, the numerator remains fixed for each region, accurately reflecting the sales percentage without being affected by the region filter directly.
References
This calculation follows Tableau's best practices for using Level of Detail expressions to manage computation granularity in the presence of dashboard filters, as outlined in the Tableau User Guide and official Tableau training materials.
NEW QUESTION # 63
A consultant plans a Tableau deployment for a client that uses Salesforce. The client wants users to automatically see Tableau views of regional sales filtered by customer as soon as the users sign into Salesforce.
Which approach should the consultant use to deliver the final visualization?
- A. Create subscriptions for each view to deliver reports by email.
- B. Embed views into Salesforce.
- C. Create a list of URLs that the users can click in Salesforce.
- D. Publish to Tableau Mobile for viewing.
Answer: B
Explanation:
To ensure that users automatically see Tableau views of regional sales filtered by customer as they sign into Salesforce, embedding the views directly into Salesforce is most effective:
Embedding Views: Tableau provides capabilities to embed its dashboards into web applications such as Salesforce. This approach ensures that the visualization is part of the Salesforce user interface, enhancing user experience by not requiring users to navigate away from Salesforce to view the data.
Implement this by using Tableau's embedding code, which can be generated from the Tableau Server for each specific view. Place this embed code into the Salesforce Visualforce pages or use Salesforce Canvas to integrate these views seamlessly.
This setup allows the Tableau views to inherit user credentials from Salesforce, enabling personalized data visualization based on the user's access rights and region, directly aligned with their Salesforce login session.
References
The embedding technique is documented in both Tableau's and Salesforce's official integration guides, which provide step-by-step instructions on embedding Tableau views into Salesforce platforms.
NEW QUESTION # 64
A client wants to migrate their Tableau Server to Tableau Cloud. The Tableau Server is configured with three sites: Finance, Strategy, and Marketing. A consultant must provide a solution that minimizes user impact and costs.
Which configuration should the consultant recommend for Tableau Cloud to meet the client's requirements?
- A. One Tableau Cloud instance configured with a Finance project folder, Strategy project folder, and Marketing project folder
- B. Three separate Tableau Cloud instances for Finance, Strategy, and Marketing
- C. One Tableau Cloud instance configured with all workbooks in a single project
- D. One Tableau Cloud instance with two sites for Strategy and Marketing, and one Tableau Server instance for Finance
Answer: A
Explanation:
To minimize user impact and costs while migrating from Tableau Server to Tableau Cloud with multiple sites, the best solution is:
Single Tableau Cloud Instance with Multiple Projects: Instead of multiple sites which could imply higher management overhead and possibly higher costs, configuring one Tableau Cloud instance with different project folders for each former site (Finance, Strategy, Marketing) is most efficient.
Benefits: This setup maintains organizational separation of data and access similar to having different sites but leverages the unified management and simplicity of a single cloud instance. It reduces complexity in user access management and integration points.
Implementation: Each project folder acts like a mini-site within the larger instance, where specific permissions and content can be managed independently, akin to the original server setup but within a single cloud-based environment.
References
This recommendation is in line with best practices for cloud migration focusing on consolidation and cost efficiency, as suggested in Tableau's official documentation for cloud migration strategies.
NEW QUESTION # 65
In what way does View Acceleration improve performance?
- A. By optimizing the performance of views built only on extract-based data sources
- B. By improving the performance of views that contain long-running queries with transient functions
- C. By enhancing the rendering speed of visuals, such as drawing shapes and maps
- D. By precompiling and fetching workbook data in a background process
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
View Acceleration is a Tableau Server and Tableau Cloud feature that speeds up slow-loading views by precomputing their queries in the background.
According to Tableau's server and cloud performance documentation:
* When View Acceleration is enabled for a view, Tableau runs the queries behind that view on a background process and stores the results in memory.
* When an end user later opens the view, Tableau can serve the precomputed results immediately, rather than running the potentially long-running queries at that moment.
* This improves initial load time significantly for views that are slow because of heavy queries.
It does not only work with extract-based data sources (it can also help with many live connections), so option A is too limited.
It does not change the client-side rendering engine, so option C is incorrect.
It is not specific to transient functions but to any view where the query is expensive, so option D is not accurate.
Therefore, the correct description is that View Acceleration precompiles and fetches workbook data in a background process, which matches option B.
* Tableau Server and Tableau Cloud help describing View Acceleration as precomputing and caching view results using background processes.
* Performance tuning guidance recommending View Acceleration for views with slow query execution.
NEW QUESTION # 66
A consultant updates an IF-THEN calculation to use a newly created calculated field "Last Name" (parsed from "Full Name"). After the change, performance becomes noticeably worse.
Which two options should the consultant use to improve dashboard performance without altering functionality? Choose two.
- A. Precalculate "Last Name" in the data source and use it.
- B. Redesign the dashboard to replace Quick Filters with Action Filters.
- C. Calculate "Last Name" in the IF THEN calculation.
- D. Change the IF THEN calculation to a CASE statement.
Answer: A,B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The performance degradation originates from string parsing inside Tableau ("last word of Full Name") and then feeding that calculated field into another row-level IF-THEN calculation.
This creates:
* Nested calculations
* High per-row evaluation load
* Slow extract query performance or slow live query generation
Tableau documentation recommends two best-practice approaches:
Solution 1: Precompute the "Last Name" field upstream (Option C)
When the parsing is performed in:
* The database
* ETL/ELT pipelines
* Tableau Prep
then Tableau Desktop receives a clean field with no runtime computation needed.
This significantly reduces row-level calculation burden.
Solution 2: Replace Quick Filters with Action Filters (Option A)
Quick filters are expensive because Tableau:
* Runs additional queries to populate filter controls
* Re-queries every time the filter changes
Action Filters run directly from the visualization and are far more performant.
This improves the overall dashboard performance without changing logic.
Why the other options are incorrect:
B). Calculate "Last Name" inside the IF THEN calculation
This makes the expression even more complex - worse performance.
D). Change to a CASE statement
CASE does not improve performance when the heavy part of the logic is the string parsing, not the IF-THEN structure.
Thus, A and C are the correct performance-improving choices.
* Performance guidance recommending upstream computation of string fields
* Filter optimization best practices encouraging Action Filters over Quick Filters
* Extract runtime cost reduction strategies
NEW QUESTION # 67
A company uses an extract built from Custom SQL joining Claims and Members.
Members have multiple records in both tables # causing data duplication, which results in inflated claim cost trends.
Which approach meets performance and maintenance goals?
- A. Use Table Calculations to ensure that claim costs are captured at the right granularity.
- B. Replace the Custom SQL with a join between two Physical Tables: Members and Claims.
- C. Use LOD calculations to ensure that claim costs are captured at the right granularity.
- D. Replace the Custom SQL with a relationship between two Logical Tables: Members and Claims.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The problem:
* Custom SQL joins two multi-row tables, causing# many-to-many duplication.
* This artificially multiplies claim costs.
* The extract becomes heavy and slow due to Custom SQL.
Tableau's recommended solution:
# Use Relationships in the Logical Layer
# Instead of physical joins
# Tableau resolves many-to-many issues automatically
# Query is generated at the appropriate granularity to avoid duplication This is exactly Option A.
Relationships allow the Claims facts to remain at the claim grain and Members to remain at the member grain.
Tableau resolves aggregations correctly, preventing inflated values.
Why the others are incorrect:
B - Physical Join
Would continue the same duplication problem because multi-row joins multiply rows.
C - LODs
Would require complex calculations and are error-prone.
They do NOT fix the duplication in the underlying extract.
D - Table Calculations
Happen after Tableau aggregates the duplicated data - too late to fix the inflated baseline numbers.
Thus, the only correct and modern solution is relationships.
* Relationships documentation explaining resolution of many-to-many granularity issues.
* Guidance recommending avoiding Custom SQL for performance reasons.
* Logical Layer behavior preventing row-duplication errors.
NEW QUESTION # 68
A Tableau consultant is tasked with creating a line graph that shows daily temperature fluctuations. The below set of data to use to create a dashboard.
How should the consultant manipulate the data to support the business need?
- A. Create a Level of Detail (LOD) calculation that will aggregate the data at the requested daily level.
- B. Request a new set of data that is aggregated to the day level.
- C. Pivot the data before the requested visualization can be created.
Answer: B
Explanation:
The business requirement is:
"Create a line graph that shows daily temperature fluctuations."
The dataset provided contains:
* Only 5 rows, one per month
* Two aggregated columns: Avg High Temp and Avg Low Temp
* No daily values in the dataset
Tableau's documentation states that:
* Tableau cannot generate artificial granularity that does not exist in the underlying data.
* LOD calculations cannot create detail that isn't present in the source. They can only roll up or fix existing grain; they cannot fabricate lower-grain data.
* Pivoting only reshapes data; it does not create missing days or introduce new rows.
* When the visualization requires detail that the dataset does not contain, the correct solution is to obtain data at the required level of granularity.
Because the dataset contains monthly averages, it is impossible to show day-to-day fluctuations without having the actual daily temperatures.
Therefore, the only way to support the business need is to request daily-level data from the data provider.
Why the other options are incorrect:
A). Pivot the data
Pivoting would convert the dataset from wide format to long format (e.g., "Avg High Temp" and "Avg Low Temp" into a single "Temperature Type" field).
This does not add daily rows, so the required daily line graph still cannot be built.
C). Create an LOD calculation
LOD expressions cannot create new lower-level detail.
They only aggregate or fix existing detail.
Because the dataset contains only monthly values, an LOD cannot generate daily temperatures.
* Tableau granularity and data modeling guidance stating that detail must exist in the data to be visualized.
* LOD expression documentation explaining that LODs cannot create lower granularity than the source data.
* Pivoting documentation explaining pivots reshape fields but do not generate new rows or finer-grain data.
NEW QUESTION # 69
A client wants to report Saturday and Sunday regardless of the workbook's data source's locale settings.
Which calculation should the consultant recommend?
- A. DATEPART('weekday', [Order Date])>=6
- B. DATENAME('iso-weekday', [Order Date])>=6
- C. DATEPART('iso-weekday', [Order Date])>=6
- D. DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7
Answer: D
Explanation:
The calculation DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7 is recommended because the ISO standard considers Monday as the first day of the week (1) and Sunday as the last day (7). This calculation will correctly identify Saturdays and Sundays regardless of the locale settings of the workbook's data source, ensuring that the report includes these days as specified by the client.
References: The use of the 'iso-weekday' part in the DATEPART function is consistent with the ISO 8601 standard, which is independent of locale settings. This approach is supported by Tableau's documentation on date functions and their behavior with different locale settings123.
To accurately identify weekends across different locale settings, using the 'iso-weekday' component is reliable as it is consistent across various locales:
ISO Weekday Function: The ISO standard treats Monday as the first day of the week (1), which makes Sunday the seventh day (7). This standardization helps avoid discrepancies in weekday calculations that might arise due to locale-specific settings.
Identifying Weekends: The calculation checks if the 'iso-weekday' part of the date is either 1 (Sunday) or 7 (Saturday), thereby correctly identifying weekends regardless of the locale settings.
References:
Handling Locale-Specific Settings: Using ISO standards in date functions allows for uniform results across systems with differing locale settings, essential for consistent reporting in global applications.
NEW QUESTION # 70
A client has a Tableau Cloud deployment. Currently, dashboards are available only to internal users.
The client needs to embed interactive Tableau visualizations on their public website.
Data is < 5,000 rows, updated infrequently via manual refresh.
Cost is a priority.
Which product should the client use?
- A. Tableau Cloud licensed per user
- B. Tableau Embedded Analytics
- C. Tableau Server licensed per core
- D. Tableau Public
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau documentation explains:
Tableau Public
* Free platform.
* Allows public sharing and embedding of fully interactive dashboards.
* Ideal for small datasets and infrequent updates.
* Does not require user-based licensing.
* Embedding is unrestricted because all content is publicly visible.
This perfectly matches the scenario:
# Public-facing website
# Low cost priority
# Small dataset
# Manual, infrequent updates
Why the other options are incorrect:
A). Tableau Cloud (per user)
* Requires paid licenses.
* Does not allow unrestricted public embedding without expensive add-ons.
* Designed for secure internal use, not public web-wide embedding.
C). Tableau Embedded Analytics
* A paid embedding solution requiring proper licensing.
* Designed for large-scale, secure, programmatic embedding - too costly for this use case.
D). Tableau Server (per core)
* Requires server infrastructure & licensing.
* Far more expensive than Tableau Public.
Thus, Tableau Public is the correct, cost-effective solution.
* Tableau Public documentation describing free embedding for public websites.
* Comparison guides showing Tableau Cloud/Server require licensing for embedding.
* Public vs. Enterprise Tableau deployment best practices.
NEW QUESTION # 71
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