Exam: | TCC-C01 | ||
Exam Name: | Tableau Certified Consultant Exam | ||
Certification(s): | Tableau Certified Consultant | ||
Questions: | 55 Questions Answers | ||
Last Updated: | Dec 15,2024 | ||
Price: | Was: $85 Today: $59 |
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Main points of Tableau TCC-C01 Test
The Tableau Certified Consultant (TCC) - C01 exam focuses on demonstrating your ability to leverage Tableau to solve complex business problems. It's not just about knowing how to use Tableau features, but also about why and when to use them effectively within a broader consulting context. Here are the main points covered:
I. Data Operations & Management:
- Connecting to and preparing data: This includes working with various data sources (relational databases, cloud data warehouses, spreadsheets), handling data types, understanding data transformations (joins, unions, pivoting, etc.), and cleaning/preparing data for analysis. This goes beyond simple drag-and-drop; you'll need to demonstrate efficient and robust data preparation strategies. Performance considerations are key.
- Data blending: Understanding when and how to blend data sources, managing the complexities of blended data, and anticipating performance challenges.
- Data governance and security: Implementing and respecting data governance best practices, including data security considerations, access controls, and understanding sensitive data handling.
II. Visual Analysis and Design:
- Choosing the right visualization: Selecting appropriate chart types based on the data and the analytical question being asked. This is about more than just aesthetics; it's about clear and effective communication of insights.
- Designing effective dashboards and stories: Creating interactive dashboards that effectively communicate key insights and facilitate exploration. Building compelling stories using Tableau's storytelling capabilities. Consider user experience and accessibility.
- Understanding visual best practices: Adhering to principles of data visualization (e.g., avoiding chartjunk, ensuring clarity and accuracy). This includes considerations for accessibility for diverse audiences.
III. Advanced Analytics and Calculations:
- Using calculated fields: Writing effective and efficient calculated fields using Tableau's scripting language (LOD expressions, table calculations, parameters). Optimizing performance of calculated fields.
- Understanding and applying table calculations: Mastering various table calculation functions and understanding their order of operations and impact on performance.
- Applying Level of Detail (LOD) calculations: Crafting complex LOD expressions to accurately answer specific analytical questions, addressing issues of aggregation and detail.
- Advanced analytics: Familiarity with concepts like forecasting, clustering, and other advanced analytic techniques within Tableau.
IV. Deployment and Administration:
- Publishing workbooks: Understanding the publishing process and managing workbooks within Tableau Server or Online.
- User management and permissions: Understanding how to manage user permissions and access control within a Tableau environment.
- Performance optimization: Identifying and addressing performance bottlenecks in dashboards and workbooks.
- Data extracts and scheduling: Managing data extracts, refreshing schedules, and optimizing data extract performance.
V. Consulting Best Practices:
- Gathering requirements: Effectively gathering and documenting business requirements from stakeholders.
- Project management: Understanding project management principles and applying them to Tableau projects.
- Communication and stakeholder management: Communicating clearly and effectively with both technical and non-technical stakeholders.
- Troubleshooting and problem-solving: Identifying and resolving issues in Tableau dashboards and workbooks.
The exam tests your ability to apply this knowledge in a practical, problem-solving context. Expect scenario-based questions that require you to choose the best approach to data preparation, visualization, or analysis. Focus on mastering the why behind the Tableau features, rather than just the how. Strong understanding of data modeling and ETL processes is also critical.
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