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Tableau TCC-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Design and Troubleshoot Calculations and Workbooks: This domain evaluates the ability to design advanced analytics and troubleshoot workbooks. Candidates will implement calculations, optimize performance, and plan interactivity for dashboards while resolving performance issues.
Topic 2	Evaluate Current State: This domain measures the skills of Tableau-certified consultants in assessing the current analytics landscape. Candidates will evaluate existing reports and data structures to ensure alignment with business needs and recommend appropriate Tableau deployment options.
Topic 3	Plan and Prepare Data Connections: This section focuses on planning data transformation and preparing connections, targeting Tableau-certified consultants. Candidates will recommend data storage methods, design row-level security structures, and troubleshoot complex data connection issues.
Topic 4	Establish Governance and Support Published Content: This section measures the skills of Tableau-certified consultants related to governance strategies within Tableau environments. Candidates will map governance requirements, recommend content access strategies, design administrative views, and identify external errors affecting performance.

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Tableau Certified Consultant Sample Questions (Q55-Q60):

NEW QUESTION # 55
A client builds a dashboard that presents current and long-term stock measures. Currently, the data is at a daily level. The data presents as a bar chart that presents monthly results over current and previous years. Some measures must present as monthly averages.
What should the consultant recommend to limit the data source for optimal performance?

A. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
B. Limit data to current and previous years as well as to the last day of each month to eliminate the need to use the averages.
C. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
D. Move calculating averages to data layer and aggregate dates to monthly level.

Answer: C

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. Use Tableau Desktop to visualize null values.
B. Use Tableau Prep to add new rows.
C. Update the Widget Inventory Table to be a daily snapshot.
D. Create a temporary table in the database.

Answer: B

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

Answer: 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 usingDATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated usingAVG(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 # 58
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 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. 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: 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.

NEW QUESTION # 59
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 filter action, set "Run action on" to Select, and set "Clearing the selection will" to Exclude all values.
B. Add a highlight action and set Target Highlighting to All Fields.
C. Add a highlight action and set Target Highlighting to Sub-Category.
D. Add a filter action, set "Run action on" to Menu, and set "Clearing the selection will" to Show all values.

Answer: A

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.
ReferencesThis 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 # 60
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