A client wants to provide sales users with the ability to perform the following tasks:
– Access published visualizations and published data sources outside the company network.
– Edit existing visualizations.
– Create new visualizations based on published data sources.
– Minimize licensing costs.
Which site role should the client assign to the sales users?
- A . Explorer (can publish)
- B . Site Administrator
- C . Viewer
- D . Creator
A
Explanation:
The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.
References: The information about the Explorer (can publish) role and its capabilities can be found in the official Tableau documentation on site roles and permissions12.This role is appropriate for users who need to interact with published content and create new visualizations without the need for full site administration or advanced content creation tools that come with the Creator role3.
A new Tableau user created a simple dashboard on Tableau Server using supply chain data. Now, the user wants to know if they created the dashboard in
accordance with specific performance best practices.
Which approach should the consultant recommend for the client to make this determination?
- A . Use inbuilt dashboards in Tableau Server to troubleshoot the performance.
- B . Use Performance Recording on Tableau Server.
- C . Use Performance Recording in Tableau Desktop.
- D . Run Workbook Optimizer.
D
Explanation:
The Workbook Optimizer is a tool specifically designed to evaluate a workbook against performance best practices. It provides feedback on key design characteristics and offers concrete guidance on how to improve workbook performance. This tool is beneficial for both new and experienced Tableau users to ensure their dashboards are optimized for performance1.
References: The Workbook Optimizer’s functionality is detailed in Tableau’s official documentation, which explains how it assesses workbooks against a set of rules derived from best practices1.Additionally, the Performance Recording feature in Tableau Desktop and Server can be used to identify performance issues, but the Workbook Optimizer gives a more comprehensive analysis of the workbook’s adherence to best practices23.
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 . Tableau Prep Conductor
- B . Ask Data
- C . Tableau Catalog
- D . Tableau Resource Monitoring Tool
C
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.
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?
- A . Strings are fastest, followed by integers, and then Booleans.
- B . Integers are fastest, followed by Booleans, and then strings.
- C . Strings, integers, and Booleans all perform the same.
- D . Booleans are fastest, followed by integers, and then strings.
B
Explanation:
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.
A client has a published data source in Tableau Server and they want to revert to the previous version of the data source. The solution must minimize the impact on users.
What should the consultant do to accomplish this task?
- A . Request that a server administrator restore a Tableau Server backup.
- B . Delete and recreate the data source manually.
- C . Select a previous version from Tableau Server, download it, and republish that data source.
- D . Select a previous version from Tableau Server, and then click Restore.
D
Explanation:
To minimize the impact on users when reverting to a previous version of a published data source in Tableau Server, the consultant should use the built-in revision history feature. By selecting a previous version from the revision history and clicking ‘Restore’, the data source will revert to that version without the need for a full server backup restoration or manual recreation of the data source. This process is quick and has the least amount of disruption to users.
References: The functionality and process for reverting to a previous version of a data source are outlined in Tableau’s official documentation on working with content revisions1.This feature is part of Tableau Server’s capabilities to manage and maintain data sources effectively21.
A client is using the Tableau Content Migration Tool to move content from an old Tableau Server to a new Tableau Server.
Which content will need to be moved using a different tool or process?
- A . Published data sources that use live connections
- B . Tableau Prep flows
- C . Published data sources that use extracts
- D . Workbooks
B
Explanation:
When migrating content between Tableau servers, certain types of content may require special consideration or different tools for migration:
Tableau Prep Flows: These are specific to Tableau Prep and are not included in the standard content migration capabilities of the Tableau Content Migration Tool. Tableau Prep flows often require separate processes for migration due to their distinct setup and integration with data sources and workflows.
Published Data Sources and Workbooks: These can typically be migrated directly using the Tableau Content Migration Tool, which supports moving published data sources (both live connections and extracts) and workbooks without requiring additional tools.
References:
Tableau Help and Support: Offers comprehensive tutorials and guidelines on using different tools for migrating various types of content, including the specific requirements for migrating Tableau Prep flows which are not covered by the standard content migration tool.
A client is using Tableau to visualize data by leveraging security token-based credentials. Suddenly, sales representatives in the field are reporting that they cannot access the necessary workbooks. The client cannot recreate the error from their offices, but they have seen screenshots from the field agents. The client wants to restore functionality for the field agents with minimal disruption.
Which step should the consultant recommend to accomplish the client’s goal?
- A . Ensure that "Allow Refresh Access" was checked when the data source was published.
- B . Change the data source permissions for the connection to "Prompt User."
- C . Ask the workbook owners to republish the workbooks to refresh the security token.
- D . Renew the security token via the Data Connection on Tableau Server.
D
Explanation:
When field agents are unable to access workbooks due to issues with security token-based credentials, the most immediate and least disruptive solution is to renew the security token. This can be done through the Data Connection settings on Tableau Server. Renewing the token will restore access for the field agents without requiring them to take any action or affecting other users.
References: The use of personal access tokens (PATs) in Tableau and the procedure for renewing them are documented in Tableau’s official resources. It is noted that PATs are long-lived authentication tokens that can be revoked and renewed to manage access securely1.Additionally, there have been discussions in the Tableau Community regarding issues with concurrent PAT access, which further supports the need to manage tokens effectively2.
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?
- A . Split some visualizations on the dashboard into many smaller visualizations on the same dashboard.
- B . Connect to the data source via a custom SQL query.
- C . Use filters, hide unused fields, and aggregate values.
OD. Restrict users from accessing the workbook to reduce server load.
C
Explanation:
To improve the performance of a Tableau workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.
References: The best practices for optimizing workbook performance in Tableau are well-documented in Tableau’s official resources, including the Tableau Help Guide and the Designing Efficient Workbooks whitepaper, which provide detailed recommendations on how to streamline workbooks for better performance12.
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?
- A . {INCLUDE [Customer ID]: COUNTD ([Order ID])}
- B . {FIXED [Customer ID], [Region]: COUNTD ([Order ID])}
- C . {EXCLUDE [Customer ID]: COUNTD ([Order ID])}
- D . {FIXED [Customer ID], [Region], [Order Date]: COUNTD ([Order ID])}
B
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.
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 change the Referential Integrity to "All
Records Match." - C . 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." - D . Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to "All Records Match."
B
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.
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.
- A . Whether or not the client plans to leverage single sign-on (SSO)
- B . Whether or not there are large numbers of concurrent extract refreshes
- C . Whether or not the client needs the ability to connect to public, cloud-based data sources
- D . Amount of data storage used on the client’s existing server
A B
Explanation:
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.
A client notices that several groups are sharing content across divisions and are not complying with their data governance strategy. During a Tableau Server audit, a consultant notices that the asset permissions for the client’s top-level projects are set to "Locked," but that "Apply to Nested Projects" is not checked.
The consultant recommends checking "Apply to Nested Projects" to enforce compliance.
Which impact will the consultant’s recommendation have on access to the existing nested projects?
- A . Current custom access will be maintained, but new custom permissions will not be granted.
- B . Access will be automatically rolled back to the top-level project permissions immediately.
- C . Users will be prompted to manually update permissions for all nested projects.
- D . Users will be notified that they will automatically lose access to content after 30 days.
B
Explanation:
When “Apply to Nested Projects” is checked in Tableau Server, the permission rules set at the top-level project are enforced for all assets in the project and all nested projects. This means that any custom access previously granted to nested projects will be overridden, and the permissions will revert to those defined at the top-level project. This action ensures consistent application of the data governance strategy across all divisions.
References: The impact of checking “Apply to Nested Projects” is detailed in Tableau’s official documentation, which explains how locked nested projects can be used to govern site content with greater flexibility and efficiency12.
A Tableau Cloud client has requested a custom dashboard to help track which data sources are used most frequently in dashboards across their site.
Which two actions should the client use to access the necessary metadata? Choose two.
- A . Connect directly to the Site Content data source within the Admin Insights project.
- B . Query metadata through the GraphiQL engine.
- C . Access metadata through the Metadata API.
- D . Download metadata through Tableau Catalog.
B C
Explanation:
To track which data sources are used most frequently across a site in Tableau Cloud, the client should use the GraphiQL engine and the Metadata API. The GraphiQL engine allows for interactive exploration of the metadata, making it easier to construct and test queries1.The Metadata API provides access to metadata and lineage of external assets used by the content published to Tableau Cloud, which is essential for tracking data source usage2.
References: The actions are based on the capabilities of the GraphiQL engine and the Metadata API as described in Tableau’s official documentation and learning resources321.
A company has a data source for sales transactions.
The data source has the following characteristics:
– Millions of transactions occur weekly.
– The transactions are added nightly.
– Incorrect transactions are revised every week on Saturday.
– The end users need to see up-to-date data daily.
A consultant needs to publish a data source in Tableau Server to ensure that all the transactions in the data source are available.
What should the consultant do to create and publish the data?
- A . Publish an incremental extract refresh every day and perform a full extract refresh every Saturday.
- B . Publish a live connection to Tableau Server.
- C . Publish an incremental refresh every Saturday.
- D . Publish an incremental extract refresh every day and publish a secondary data set containing data revisions.
A
Explanation:
Given the need for up-to-date data on a daily basis and weekly revisions, the best approach is to use an incremental extract refresh daily to update the data source with new transactions. On Saturdays, when incorrect transactions are revised, a full extract refresh should be performed to incorporate all revisions and ensure the data’s accuracy. This strategy allows end users to have access to the most current data throughout the week while also accounting for any necessary corrections12.
References: The solution is based on best practices for managing data sources in Tableau Server, which recommend using incremental refreshes for frequent updates and full refreshes when significant changes or corrections are made to the data12.
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?
- A . Use a calculation with TOTAL() function instead of a quick table calculation.
- B . Add the [Profit] filter to the context.
- C . Filter with a table calculation WINDOW_AVG(MIN([Profit]), first(), last())
- D . Filter with the table calculation used to create labels.
B
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.
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’ ELSE ‘Unprofitable’
END - C . IF [ProfitMargin]>0.50 Then ‘Highly Profitable’
ELSEIF [ProfitMargin]>=0 Then ‘Profitable’
ELSEIF [ProfitMargin] <0 Then ‘Unprofitable’
END - D . IF([ProfitMargin]>=0.50,’Highly Profitable’, ‘Profitable’) ELSE ‘Unprofitable’
END
B
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.
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 (‘month’, DATEADD(‘month’, 1, [Order Date]))
Calc2: AVG(DATEDIFF (‘week’, [Order Date], [Calc1])) - B . Calc1: DATETRUNC (‘month’, DATEADD (‘month’, 1, [Order Date]))
Calc2: AVG(DATEDIFF (‘day’, [Order Date], [Calc1])) - C . Calc1: DATETRUNC (‘day’, DATEADD(‘week’, 4, [Order Date]))
Calc2: AVG([Order Date] – [Calc1]) - D . Calc1: DATETRUNC (‘day’, DATEADD (‘day’, 31, [Order Date]))
Calc2: AVG ([Order Date] – [Calc1])
B
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.
For a new report, a consultant needs to build a data model with three different tables, including two that contain hierarchies of locations and products. The third table contains detailed warehousing data from all locations across six countries. The consultant uses Tableau Cloud and the size of the third table excludes using an extract.
What is the most performant approach to model the data for a live connection?
- A . Relating the tables in Tableau Desktop
- B . Blending the first two tables with the third
- C . Joining the tables in Tableau Prep
- D . Joining the tables in Tableau Desktop
A
Explanation:
For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau’s relationships feature, which optimizes queries for the underlying database.
References: The best practices for live connections in Tableau Cloud suggest using relationships to manage complex data models efficiently1.Additionally, Tableau’s documentation on connecting data sources recommends using relationships for better performance with live connections2.
A client uses Tableau Data Management and notices that when they view a data source, they sometimes see a different count of workbooks in the Connected Workbooks tab compared to the lineage count in Tableau Catalog.
What is the cause of this discrepancy?
- A . Some workbooks have been connected to the data source, but do not use any fields from it.
- B . Some workbooks have not been viewed by enough users yet.
- C . Some of the workbooks connected to the data source are not visible to the user due to permissions.
- D . Some Creators have connected to the data source in Tableau Desktop but have not yet published a workbook.
C
Explanation:
The discrepancy between the count of workbooks in the Connected Workbooks tab and the lineage count in Tableau Catalog can occur because of user permissions. In Tableau Data Management, the visibility of
connected workbooks is subject to the permissions set by administrators. If a user does not have permission to view certain workbooks, they will not see them listed in the Connected Workbooks tab, even though these workbooks are part of the data source’s lineage and are counted in Tableau Catalog.
References: This Explanation: is based on the functionality of Tableau Data Management and Tableau Catalog, which includes managing user permissions and access to workbooks. The information is supported by Tableau’s official documentation on data management and security practices1.
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 . DATEPART(‘iso-weekday’, [Order Date])>=6
- C . DATENAME(‘iso-weekday’, [Order Date])>=6
- D . DATEPART(‘iso-weekday’, [Order Date])=1 or DATEPART(‘iso-weekday’, [Order Date])=7
D
Explanation:
The calculation DATEPART (‘iso-weekday’, [Order Date])=1 or DATEPART(‘iso-weekday’, [Order Date])=7is 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.