Web-based TCC-C01 Practice Test With Dumps

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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	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	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.

Tableau Certified Consultant Sample Questions (Q35-Q40):

NEW QUESTION # 35
A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that provides the best user experience across all the platforms.
Which approach should the consultant take to achieve these results?

A. Build one dashboard that has desktop, tablet, and phone layouts, and fix the size of the layouts.
B. Build one dashboard and set the size to Automatic.
C. Build one dashboard and fix the size of the dashboard.
D. Build one dashboard for each type of device and fix the size of the layouts.

Answer: A

Explanation:
For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:
* Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.
* Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices. Fixed sizes prevent components from resizing in ways that could disrupt the dashboard's readability or functionality.
* Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.
ReferencesThis approach leverages Tableau's device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau's official guides on creating and managing device-specific dashboards.

NEW QUESTION # 36
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?

A. Define the filters by using custom SQL.
B. Set up the extract as an incremental refresh.
C. Aggregate the extract to the visible dimensions.
D. Use an extract filter.

Answer: D

Explanation:
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References:The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts.These practices suggest keeping the extract's data set short through filtering1.Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
* Extract Filter: This involves setting filters that apply directly when the data is extracted from the source. This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
* To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to
* "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.

NEW QUESTION # 37
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 Sub-Category.
B. Add a filter action, set "Run action on" to Select, and set "Clearing the selection will" to Exclude all values.
C. Add a highlight action and set Target Highlighting to All Fields.
D. Add a filter action, set "Run action on" to Menu, and set "Clearing the selection will" to Show all values.

Answer: B

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 # 38
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. POWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
- 1
END
B. CASE [Sector Parameter]
WHEN 1 THEN "green"
WHEN 2 THEN "yellow"
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 # 39
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. Download metadata through Tableau Catalog.
D. Access metadata through the Metadata API.

Answer: B,D

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.

NEW QUESTION # 40
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Web-based TCC-C01 Practice Test With Dumps

Valid Test TCC-C01 Tips, TCC-C01 Exam Topic

Tableau TCC-C01 Exam Syllabus Topics:

Tableau Certified Consultant Sample Questions (Q35-Q40):

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