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Many candidates find the Tableau exam preparation difficult. They often buy expensive study courses to start their Tableau TCC-C01 certification exam preparation. However, spending a huge amount on such resources is difficult for many Tableau Certified Consultant applicants. The Latest TCC-C01 Exam Dumps are the right option for you to prepare for the TCC-C01 certification test at home.

Tableau TCC-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.
Topic 4	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.

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Tableau Certified Consultant Sample Questions (Q29-Q34):

NEW QUESTION # 29
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 Catalog
C. Tableau Resource Monitoring Tool
D. Tableau Prep Conductor

Answer: B

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 # 30
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 ('day', DATEADD('week', 4, [Order Date]))
Calc2: AVG([Order Date] - [Calc1])
C. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))
D. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
Calc2: AVG ([Order Date] - [Calc1])

Answer: C

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 # 31
A client is working in Tableau Prep and has a field named Orderld that is compiled by country, year, and an order number as shown in the following table.

What should the consultant use to transform the table in the most efficient manner?

A. The Aliases option
B. A calculated field that uses the TRIM function
C. A calculated field that uses the LEFT function
D. The Split option

Answer: D

Explanation:
To transform theOrderldfield in Tableau Prep, the Split option is the most efficient and straightforward method. Here's how you can apply it:
* In Tableau Prep, drag your dataset into the flow.
* Click on theOrderldfield in the workspace to select it.
* Look for the option in the toolbar that says "Split" and select it.
* Choose "Automatic Split" if the delimiters (such as hyphens) are consistent; Tableau Prep should automatically detect the hyphen as the delimiter and split theOrderldinto multiple new fields.
* The dataset should now show new columns: one for the country code (CA, FR, US), one for the year (2017), and one for the order number (152156, 152157, etc.).
The Split option works effectively here because it automatically identifies and uses the hyphen as the delimiter to divide the originalOrderldinto the desired components without manual specification of conditions or writing any formulas.
ReferencesThis procedure is based on the standard functionalities provided in Tableau Prep for splitting a field into multiple columns based on a delimiter, as described in the Tableau Prep user guide.

NEW QUESTION # 32
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which calculation should the consultant use?

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

Answer: C

Explanation:
To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:
* Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.
* Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre-calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.
References:
* Materialization in Extracts: This concept involves pre-calculating and storing complex aggregations or calculations within the Tableau data extract itself, improving performance by reducing the computational load during visualization rendering.

NEW QUESTION # 33
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 Performance Recording on Tableau Server.
B. Run Workbook Optimizer.
C. Use Performance Recording in Tableau Desktop.
D. Use inbuilt dashboards in Tableau Server to troubleshoot the performance.

Answer: B

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.

NEW QUESTION # 34
......

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FANTASTIC TCC-C01 LATEST TEST BOOTCAMP & LEADER IN QUALIFICATION EXAMS & UNPARALLELED TEST TCC-C01 QUESTIONS PDF

Fantastic TCC-C01 Latest Test Bootcamp & Leader in Qualification Exams & Unparalleled Test TCC-C01 Questions Pdf