TOP TCC-C01 CLEARER EXPLANATION - VALID TABLEAU TABLEAU CERTIFIED CONSULTANT - TEST TCC-C01 PATTERN

TOP TCC-C01 Clearer Explanation - Valid Tableau Tableau Certified Consultant - Test TCC-C01 Pattern

TOP TCC-C01 Clearer Explanation - Valid Tableau Tableau Certified Consultant - Test TCC-C01 Pattern

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Tableau TCC-C01 Exam Syllabus Topics:

TopicDetails
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 (Q32-Q37):

NEW QUESTION # 32
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. Amount of data storage used on the client's existing server
  • B. Whether or not the client plans to leverage single sign-on (SSO)
  • C. Whether or not the client needs the ability to connect to public, cloud-based data sources
  • D. Whether or not there are large numbers of concurrent extract refreshes

Answer: B,D

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.


NEW QUESTION # 33
A stakeholder has multiple files saved (CSV/Tables) in a single location. A few files from the location are required for analysis. Data transformation (calculations) is required for the files before designing the visuals. The files have the following attributes:
. All files have the same schema.
. Multiple files have something in common among their file names.
. Each file has a unique key column.
Which data transformation strategy should the consultant use to deliver the best optimized result?

  • A. Use wildcard Union option to combine/merge all the files together before doing the data transformation (calculations).
  • B. Use join option to combine/merge all the files together before doing the data transformation (calculations).
  • C. Apply the data transformation (calculations) in each require file and do the wildcard union to combine/merge before designing the visuals.
  • D. Apply the data transformation (calculations) in each require file and do the join to combine/merge before designing the visuals.

Answer: A

Explanation:
Moving calculations to the data layer and materializing them in the extract can significantly improve the performance of reports in Tableau. The calculationZN([Sales])*(1 - ZN([Discount]))is a basic calculation that can be easily computed in advance and stored in the extract, speeding up future queries.This type of calculation is less complex than table calculations or LOD expressions, which are better suited for dynamic analysis and may not benefit as much from materialization12.
References:The answer is based on the best practices for creating efficient calculations in Tableau, as described in Tableau's official documentation, which suggests using basic and aggregate calculations to improve performance1.Additionally, the process of materializing calculations in extracts is detailed in Tableau's resources2.
Given that all files share the same schema and have a common element in their file names, the wildcard union is an optimal approach to combine these files before performing any transformations. This strategy offers the following advantages:
* Efficient Data Combination: Wildcard union allows multiple files with a common naming scheme to be combined into a single dataset in Tableau, streamlining the data preparation process.
* Uniform Schema Handling: Since all files share the same schema, wildcard union ensures that the combined dataset maintains consistency in data structure, making further data manipulation more straightforward.
* Pre-Transformation Combination: Combining the files before applying transformations is generally more efficient as it reduces redundancy in transformation logic across multiple files. This means transformations are written and processed once on the unified dataset, rather than repeatedly for each individual file.
References:
* Wildcard Union in Tableau: This feature simplifies the process of combining multiple similar files into a single Tableau data source, ensuring a seamless and efficient approach to data integration and preparation.


NEW QUESTION # 34
A client has a pipeline dashboard that takes a long time to load. The dashboard is connected to only one large data source that is an extract.
It contains two calculated fields:
. TOTAL([Opportunities])
SUM([Value])
It also contains two filters:
. A Relative Date filter on Created Date, a Date field containing values from 5 years ago until today
. A Multiple Values (Dropdown) filter on Account Name, a String field containing 1,000 distinct values A consultant creates a Performance Recording to troubleshoot the issue, and finds out that the longest-running event is "Executing Query." Which step should the consultant take to resolve this issue?

  • A. Replace the TOTAL([Opportunities]) calculation with a Grand Total.
  • B. Replace the Multiple Values (Dropdown) filter with a Multiple Values (Custom List) filter.
  • C. Replace the Relative Date filter with a Multiple Values (Dropdown) filter on YEAR([Created Date]).
  • D. Replace SUM([Value]) with WINDOW_SUM([Value]).

Answer: C

Explanation:
To improve the loading time of the pipeline dashboard, which primarily suffers from long query execution times due to a comprehensive Relative Date filter:
* Relative Date Filter Issue: The existing Relative Date filter on "Created Date" covers a broad range (5
* years), leading to significant data processing overhead as it includes granular date calculations over a large dataset.
* Optimized Approach: By replacing the Relative Date filter with a Multiple Values (Dropdown) filter based on YEAR([Created Date]), the filter granularity is reduced. Filtering by year simplifies the query by limiting the volume of data processed and reducing the complexity of the filter condition.
* Implementation Benefit: This approach still provides the flexibility to view data across different years but does so by reducing the load on the database during query execution, which is critical for improving the performance of the dashboard.
ReferencesThis recommendation aligns with Tableau performance optimization strategies, specifically regarding the management of date filters to minimize their impact on query load, as discussed in Tableau performance tuning sessions and guides.


NEW QUESTION # 35
A client currently has a workbook with the table shown below.

Which method will produce the output for the Total Sales Value field for all the categories shown in the table?

  • A. Level of Detail (LOD) Calculation
  • B. A Window Function
  • C. MAX() Function
  • D. Quick Table Calculation

Answer: A

Explanation:
To calculate the Total Sales Value for all categories as displayed in the table, an LOD expression is ideal. An LOD calculation in Tableau allows you to compute values at the data level that is different from the view level. In this case, since the Total Sales Value appears consistent across different sub-categories within each category, an LOD expression can be used to fix the Total Sales Value irrespective of the sub-category detail.
Here's how to set it up:
* Go to the Calculations area by right-clicking in the data pane and selecting "Create Calculated Field".
* Enter a name for the calculation, such as "Total Sales Value".
* Enter the LOD expression:{ FIXED [Category] : SUM([Sales]) }. This calculation fixes the total sales to the category level, effectively summing sales for all sub-categories within each category, irrespective of how the data is broken down in the view.
* Drag this new calculated field into your visualization alongside the existing measures.
This method ensures that the Total Sales Value reflects the total for each category across all its sub-categories, matching the uniform values shown across different rows for each category in your table.
ReferencesThe explanation utilizes the concept of Level of Detail calculations in Tableau, which allows for advanced aggregations independent of the view level details. This concept is covered extensively in Tableau's official documentation and relevant training materials such as Tableau's online help resources.


NEW QUESTION # 36
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 Creators have connected to the data source in Tableau Desktop but have not yet published a workbook.
  • 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 workbooks have been connected to the data source, but do not use any fields from it.

Answer: 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.


NEW QUESTION # 37
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