Tableau Interview Question List Part1

Tableau Interview Question List Preperation

Tableau Interview Question List

Give me an example of one of the complex Dashboards you created in you project, duration, challenges you faced?
What are Tableau Actions and which one you used
What is use of (.tps) file , Tableau Preferences?
Have you ever used Forecast what feature you used what is limitation?
How to view underlying SQL Queries in Tableau?
What is disaggregation and when to use it ?
Tableau Filters ? Please mention all filters as per order of operation means first to last.
What is Extract Filters ,Data Source Filters, Context Filters, Dimension Filters, Measure Filters
In what scenario LOD you used and what is significant of LOD?
Data blending and limitation of Data blending?
What is Content Filter and why it is used and when we should not use ?
Have you ever refreshed extract through shell script?
Scenario-

If you have 10 GB of extract and you tried to upload on server it get failed due to policy of space limitation, how to overcome this situation?

One dashboard is in PROD with over 5 M rows , what are steps will you follow for Performance tuning?
Tableau Admin: Have performed any server upgrades and what steps involved in it?

Management

Have you led any Tableau developer team?
How did you tracked requirement? Any template ?

What is KPI(Key Perfromance Indicator) in tableau?

In Tableau, KPI stands for Key Performance Indicator. KPIs are visual elements used to measure and evaluate the performance of specific metrics or goals within a business or organization. They provide a quick and easily understandable representation of performance against targets or benchmarks. Tableau allows users to create KPIs as part of their visualizations to track progress and make data-driven decisions.

Here's how KPIs work in Tableau:

Metric Selection: First, you need to identify the metric or key performance indicator you want to track. This could be a sales target, customer satisfaction score, website traffic, or any other important metric for your organization.
Define Thresholds: Once you have the metric, you need to define the thresholds or benchmarks for your KPI. These thresholds typically include a target value, warning value, and critical value. The target value represents the desired or expected performance, while the warning and critical values indicate performance levels that require attention.
Visualization: In Tableau, you can create a visualization that represents your KPI. This could be a simple number, a gauge, or any other visual representation that effectively communicates the performance against the defined thresholds. Tableau provides a variety of visualization options to choose from.
Color Coding: To enhance the clarity and impact of your KPI, you can apply color coding. For example, you can use green for values that meet or exceed the target, yellow for values that fall within the warning range, and red for values that are below the critical threshold. Color coding helps users quickly identify performance levels.
Interactivity: Tableau allows you to add interactivity to your KPIs. Users can interact with the visualization to explore underlying data, filter based on different dimensions or time periods, and drill down to get more detailed insights. This flexibility enables users to dig deeper into the performance metrics and understand the factors affecting the KPIs.
Dashboard Integration: KPIs can be incorporated into dashboards in Tableau. Dashboards provide a consolidated view of multiple KPIs, allowing users to monitor performance across different metrics and make comparisons. Dashboards can be shared with stakeholders and updated in real-time to ensure everyone has access to the latest performance information.

By utilizing KPIs in Tableau, organizations can effectively monitor their progress, identify areas that need attention, and make informed decisions based on data. Tableau's powerful visualization capabilities and interactive features enable users to create dynamic and impactful KPIs that drive better performance management and decision-making processes.

What are steps to develop Tableau dashboards?

Developing Tableau dashboards involves several steps to ensure the effective design, functionality, and usability of the dashboard. Here are the general steps to develop Tableau dashboards:

Define Dashboard Objectives: Determine the purpose and objectives of the dashboard. Understand the key metrics, audience, and the insights you want to convey. Identify the questions the dashboard should answer and the actions it should drive.
Gather and Prepare Data: Collect the relevant data for your dashboard. Ensure that the data is clean, accurate, and in a format that Tableau can work with. Perform necessary data transformations, aggregations, and calculations to prepare the data for visualization.
Plan Dashboard Layout: Plan the layout and structure of your dashboard. Determine the number and arrangement of visualizations, the placement of filters, titles, and any other supporting elements. Consider the logical flow of information and the visual hierarchy to guide users' attention.
Create Visualizations: Build individual visualizations using the Tableau interface. Choose appropriate chart types, configure data sources, and apply necessary calculations and filters. Use colors, labels, and formatting to enhance the visual appeal and clarity of the visualizations.
Arrange Visualizations on the Dashboard: Bring together the individual visualizations onto a single dashboard canvas. Use the drag-and-drop functionality in Tableau to position and resize the visualizations. Ensure that the layout is logical, aesthetically pleasing, and optimized for the desired user experience.
Add Interactivity and Filters: Enhance the user experience by adding interactive elements to the dashboard. Incorporate filters, parameters, and actions to allow users to explore the data, filter information based on their requirements, and drill down into specific details.
Apply Formatting and Styling: Apply consistent formatting and styling to the dashboard elements. Choose appropriate colors, fonts, and sizes to create a cohesive and visually appealing design. Ensure that the dashboard is easily readable and accessible to users.
Test and Refine: Test the functionality and usability of the dashboard. Check for any data inconsistencies, formatting issues, or performance bottlenecks. Gather feedback from users and stakeholders and make necessary refinements to improve the effectiveness of the dashboard.
Publish and Share: Once the dashboard is finalized, publish it to Tableau Server, Tableau Public, or any other desired platform. Configure access permissions and sharing settings. Communicate and distribute the dashboard to the intended audience, ensuring it reaches the right stakeholders.
Monitor and Maintain: Regularly monitor the usage and performance of the dashboard. Update the data sources and visualizations as needed. Incorporate user feedback and address any emerging needs or changes in the reporting requirements.

Remember, these steps provide a general framework for developing Tableau dashboards. The specific process may vary depending on the complexity of the dashboard, the data sources involved, and the unique requirements of your organization or project. It's important to iterate, experiment, and continuously improve the dashboard based on user feedback and evolving business needs.

Please explain tableau server?

Tableau Server is a web-based platform provided by Tableau that allows organizations to share, collaborate, and distribute Tableau visualizations and dashboards securely. It is designed to enable seamless access to Tableau content for a wide range of users within an organization, regardless of their location or device. Tableau Server acts as a central hub for hosting and managing Tableau content, providing a secure and scalable environment for data sharing and collaboration.

Here are some key features and functionalities of Tableau Server:

Centralized Content Repository: Tableau Server serves as a centralized repository for Tableau content, including dashboards, workbooks, data sources, and extracts. Users can publish their Tableau content to the server, making it accessible to others within the organization.
Web-Based Access: Tableau Server provides a web-based interface that allows users to access Tableau content through their preferred web browser. This eliminates the need to install Tableau Desktop or any other client software on individual devices, making it easy to access and share visualizations across different platforms.
Secure Data Sharing: Tableau Server offers robust security features to ensure that data is shared securely within the organization. Administrators can control user access, set permissions and authentication methods, and define user roles and groups to manage data governance effectively.
Collaboration and Interaction: Tableau Server facilitates collaboration among users by allowing them to comment on dashboards, share insights, and engage in discussions around the data. Users can interact with visualizations, apply filters, and drill down into data to gain deeper insights and explore different perspectives.
Mobile Access: Tableau Server provides a responsive and touch-friendly interface, enabling users to access and interact with Tableau content on mobile devices such as smartphones and tablets. This allows users to stay connected to their data and visualizations while on the go.
Scheduling and Data Refresh: Tableau Server offers scheduling capabilities, allowing administrators to automate data refreshes and updates for published content. This ensures that the data displayed in dashboards and reports is up to date without manual intervention.
Governance and Administration: Tableau Server provides a comprehensive set of administrative tools and functionalities. Administrators can manage user access, permissions, and licenses, monitor server performance and usage, and track content usage and popularity. They can also enforce data governance policies and maintain data integrity.
Scalability and Performance: Tableau Server is designed to handle large volumes of data and support a growing user base. It offers scalability options to accommodate increasing data and user demands. Additionally, Tableau Server leverages the in-memory processing capabilities of Tableau Data Engine (Hyper) to deliver fast and interactive visualizations.
Embedding and Integration: Tableau Server allows for the embedding of Tableau visualizations into external websites, portals, or other applications. It also offers APIs (Application Programming Interfaces) and SDKs (Software Development Kits) for integration with other systems, enabling developers to extend the functionality of Tableau Server.

Tableau Server empowers organizations to share insights, promote data-driven decision-making, and foster collaboration across teams. Its web-based accessibility, security features, and robust administration capabilities make it a preferred choice for deploying and managing Tableau content in enterprise environments.

what is Tableau Desktop?

Tableau Desktop is a powerful data visualization and analysis software offered by Tableau. It is a feature-rich application that allows users to connect to different data sources, transform and manipulate data, and create interactive visualizations and dashboards. Tableau Desktop is primarily used by data analysts, business intelligence professionals, and data scientists to explore, analyze, and present data in a visually appealing and meaningful way.

Here are some key features and capabilities of Tableau Desktop:

Data Connection: Tableau Desktop supports a wide range of data sources, including databases, spreadsheets, cloud services, and big data platforms. Users can connect to multiple data sources simultaneously, blend and join data, and create relationships between tables.
Data Preparation: Tableau Desktop provides tools for data preparation and cleansing. Users can perform tasks such as filtering, sorting, pivoting, aggregating, and transforming data to ensure it is in the desired format for analysis.
Drag-and-Drop Interface: Tableau Desktop offers an intuitive drag-and-drop interface, allowing users to easily build visualizations without the need for complex coding or scripting. Users can drag fields onto shelves to create charts, graphs, maps, and other visual elements.
Interactive Visualizations: Tableau Desktop enables the creation of interactive visualizations that respond to user interactions. Users can filter, drill down, and explore data by interacting with the visualizations. This interactivity enhances the data exploration experience and enables users to uncover insights.
Calculations and Analytics: Tableau Desktop provides a variety of built-in calculations, functions, and statistical capabilities. Users can create calculated fields, perform advanced analytics, and apply statistical models to gain deeper insights from the data.
Dashboards and Stories: Tableau Desktop allows users to combine multiple visualizations into interactive dashboards. Dashboards provide a consolidated view of different visualizations and allow users to explore data holistically. Additionally, Tableau Desktop enables the creation of stories, which are a sequence of interactive visualizations that guide users through a narrative or analysis.
Formatting and Design: Tableau Desktop offers a range of formatting and design options to customize the appearance of visualizations. Users can apply color schemes, fonts, and styles to enhance the visual appeal of their dashboards and ensure consistency in branding.
Collaboration and Sharing: Tableau Desktop provides options for sharing and collaboration. Users can publish their visualizations to Tableau Server or Tableau Online, allowing others to access and interact with the visualizations. Sharing can be done through links, embedded in websites, or exported as image or PDF files.

Tableau Desktop empowers users to explore data, uncover insights, and communicate findings effectively through compelling visualizations. Its intuitive interface, extensive data connectivity, and interactive capabilities make it a popular choice for data visualization and analysis across industries.

What is Tableau Extracts? How it better than live connection?

Tableau Extracts, also known as Tableau Data Extracts, are a powerful feature in Tableau that allow you to extract and store a subset of your data from its original source into a highly optimized and compressed format. Here's an explanation of Tableau Extracts and how they offer advantages over live connections:

Data Performance: Tableau Extracts are specifically designed to enhance performance. When you create an extract, Tableau aggregates and indexes the data, making it easier and faster for Tableau to query and analyze. Extracts are stored in a columnar format, which enables efficient data compression and retrieval. As a result, extracting data into Tableau can significantly improve the speed of visualizations and data exploration.
Offline Access: One of the main benefits of Tableau Extracts is that they allow for offline access to data. Extracts are stored locally on Tableau Desktop or Tableau Server, meaning users can access and interact with the data even without a live connection to the original data source. This is especially useful in scenarios where internet connectivity may be limited or unreliable.
Improved Data Source Performance: By utilizing Tableau Extracts, you can alleviate the strain on your data source. When using live connections, Tableau queries the data source in real-time, potentially causing performance issues if the data source is large or under heavy usage. Extracts offload this processing to Tableau itself, reducing the workload on the data source and improving its performance for other applications and users.
Data Subset and Filtering: Tableau Extracts allow you to create subsets of your data by applying filters and aggregations during the extraction process. This means you can extract only the necessary data required for your analysis, reducing the overall data volume and improving performance further. Extracts can also be set to automatically update or refresh on a schedule, ensuring that the extracted data remains up to date.
Data Security: Extracts offer an additional layer of data security. Since the data is stored within Tableau itself, you can control access to the extracts independently from the original data source. This allows you to set specific permissions and restrict access to sensitive data, providing greater control over data security and compliance.
Data Blending and Joining: Tableau Extracts enable blending and joining of data from multiple sources directly within Tableau. You can combine data from different data sources, such as databases, spreadsheets, or cloud services, into a single extract. This facilitates comprehensive analysis and visualization of integrated data without relying on complex joins or live connections.
Enhancing Performance on Large Datasets: In cases where the original dataset is extremely large, Tableau Extracts can significantly improve performance compared to live connections. Extracts are optimized for speed and can handle large volumes of data more efficiently, enabling faster queries and analysis.

While Tableau Extracts offer numerous advantages, it's worth noting that they require additional storage space on the local machine or server, and they need to be refreshed periodically to reflect changes in the original data. The decision to use Tableau Extracts versus live connections depends on factors such as the size and complexity of the data, performance requirements, data availability, and security considerations.

What is Tableau blending? What is Data blending.

Tableau blending, also known as data blending, is a feature in Tableau that allows you to combine data from multiple data sources or tables within a single visualization or worksheet. It enables you to analyze and visualize data that is stored in separate sources or tables without the need to merge or join them into a single dataset.

Here's how Tableau blending works:

Connecting to Multiple Data Sources: With Tableau, you can connect to multiple data sources simultaneously, such as databases, spreadsheets, cloud services, or web connectors. Each data source can contain different tables or data structures.
Creating Separate Data Connections: When you connect to each data source, Tableau establishes separate data connections for each source. This means that the data from each source is treated as independent data sources within Tableau.
Dragging Fields to Worksheets: Once you have established the data connections, you can start building visualizations by dragging fields from different data sources onto the worksheet. Tableau automatically detects the relationships between the fields and visualizes the data accordingly.
Blending Data: When you drag fields from different data sources onto a visualization, Tableau blends the data based on the common dimensions or fields. Tableau uses these common fields to link the data sources and align the data points, enabling the blending of data.
Handling Different Granularities: Blending data in Tableau allows you to work with data sources that have different granularities or levels of detail. Tableau intelligently handles these differences by dynamically aggregating or disaggregating the data, ensuring that the visualizations are accurate and meaningful.
Combining Data in Visualizations: By blending data, you can create visualizations that combine data from multiple sources, allowing you to compare and analyze different datasets within a single chart or dashboard. This enables you to gain insights and identify relationships or patterns that may exist across different data sources.

Tableau blending is particularly useful when you have multiple data sources that cannot be easily joined or merged into a single dataset. It allows you to work with diverse data sources and leverage their unique strengths without the need for complex data integration or preprocessing. However, it's important to note that blending data may have performance implications, especially when dealing with large datasets or when there are many data connections involved. Proper data source optimization and consideration of performance factors are important when working with blended data in Tableau.

what are limitations of data blending in tableau?

While data blending in Tableau offers flexibility in analyzing and visualizing data from multiple sources, it does have some limitations. Here are some of the key limitations of data blending in Tableau:

Performance Impact: Blending data from multiple sources can have an impact on performance, especially when dealing with large datasets or complex relationships. Tableau needs to perform additional computations and data matching operations to blend the data, which can slow down query response times and visualization rendering.
Limited Join Types: Tableau's data blending supports a limited set of join types, mainly left and inner joins. This means that some complex join operations, such as full outer joins or right joins, may not be possible using data blending alone. If more advanced join operations are required, it may be necessary to preprocess and join the data outside of Tableau before blending.
Limited Aggregation Options: Data blending in Tableau has limitations when it comes to aggregating data from multiple sources. It primarily supports aggregated measures from the secondary data sources, and it can be challenging to perform complex aggregations or calculations involving multiple data sources. In such cases, using data blending in combination with calculated fields or Tableau's Level of Detail (LOD) expressions may be necessary.
Data Incompatibility: Data blending requires a common field or dimension between the data sources to blend them successfully. If the data sources have incompatible field types or inconsistent values in the common fields, data blending may not work as expected. Data preparation and cleaning may be required to align the field types and values before blending.
Limited Flexibility in Data Transformation: Data blending in Tableau does not provide extensive data transformation capabilities. You cannot perform complex data transformations or calculations directly on the blended data. To overcome this limitation, you may need to create calculated fields or use Tableau's data preparation tools to transform the data before blending.
Lack of Dynamic Relationships: Unlike joins in a database, data blending in Tableau does not establish dynamic relationships between the blended data sources. As a result, changes in one data source may not automatically propagate to other blended data sources. You may need to manually refresh or update the blended data if changes occur in the underlying data sources.
Limited Cross-Data Source Filtering: Filtering data from multiple data sources can be challenging with data blending. Tableau allows filtering at the individual data source level but does not provide a straightforward way to apply global filters that affect all blended data sources simultaneously. This can limit the ability to create consistent and synchronized filtering across the blended data.

Despite these limitations, Tableau's data blending feature remains a valuable tool for combining and analyzing data from multiple sources within a single visualization. It's important to consider these limitations and assess whether data blending is the most suitable approach for your specific data integration and analysis requirements.

How to gather Tableau Dashboard Requirements?

Gathering Tableau dashboard requirements is a crucial step in the dashboard development process. It involves understanding the needs, objectives, and expectations of the stakeholders who will be using the dashboard. Here are some steps to help you gather Tableau dashboard requirements effectively:

Identify Stakeholders: Determine the key stakeholders who will be using the dashboard. This can include executives, managers, analysts, and other relevant users. Each stakeholder may have different roles, responsibilities, and requirements, so it's important to involve all relevant parties.
Conduct Interviews and Workshops: Schedule interviews or workshops with the stakeholders to gather their input. Ask open-ended questions to understand their goals, data needs, desired insights, and any specific functionality they require in the dashboard. Use this opportunity to capture their pain points and challenges related to data analysis and decision-making.
Understand Business Objectives: Gain a clear understanding of the business objectives that the dashboard is intended to support. This could be improving operational efficiency, tracking key performance indicators (KPIs), identifying trends, supporting decision-making, or any other specific objectives. Aligning the dashboard with these objectives will help ensure its relevance and usefulness.
Identify Data Sources: Determine the data sources that will be used to populate the dashboard. Understand the availability and accessibility of the data sources, as well as any data integration or preprocessing requirements. Identify any specific data fields or metrics that are critical for the stakeholders' analysis and decision-making.
Define Key Metrics and Visualizations: Based on the stakeholders' requirements and objectives, identify the key metrics and visualizations that should be included in the dashboard. Determine the types of charts, graphs, tables, and other visual elements that will best represent the data and provide insights. Consider the level of detail, granularity, and interactivity required by the stakeholders.
Determine Dashboard Interactivity: Understand the desired level of interactivity in the dashboard. Determine if stakeholders want the ability to filter and drill down into the data, apply different dimensions or measures, and customize views. Identify any specific filters, parameters, or actions that should be incorporated into the dashboard to enhance interactivity.
Design and Layout Preferences: Discuss the stakeholders' preferences regarding the design and layout of the dashboard. Understand their branding guidelines, color schemes, and any specific visualization preferences they have. Consider their preferences for dashboard organization, navigation, and overall user experience.
Document Requirements: Document all the gathered requirements in a clear and structured manner. Create a requirements document that includes the stakeholders' needs, objectives, data sources, metrics, visualizations, interactivity, and design preferences. This document will serve as a reference throughout the dashboard development process.
Validate and Iterate: Share the requirements document with the stakeholders for their review and validation. Seek their feedback and clarify any ambiguities or gaps. Iterate on the requirements as necessary to ensure that they accurately capture the stakeholders' expectations and needs.

What are Tableau features? Explain in brief.

Tableau is a powerful data visualization and business intelligence tool that offers a range of features to help users analyze, visualize, and share data effectively. Here are some key features of Tableau:

Data Connection and Integration: Tableau allows you to connect to various data sources, including databases, spreadsheets, cloud services, and web connectors. It supports direct connections to popular data platforms, and it provides options for data blending and joining to combine data from multiple sources.
Drag-and-Drop Interface: Tableau's intuitive drag-and-drop interface makes it easy to create interactive visualizations without the need for complex coding or scripting. You can simply drag fields onto the canvas to create charts, graphs, maps, and tables.
Visual Analytics: Tableau offers a wide range of visualizations, including bar charts, line charts, scatter plots, heat maps, treemaps, and more. It provides extensive customization options to control colors, labels, tooltips, and formatting to create visually appealing and meaningful visualizations.
Dashboards and Stories: Tableau allows you to create interactive dashboards by combining multiple visualizations into a single canvas. Dashboards provide an overview of data and enable users to interactively explore and analyze information. Additionally, Tableau allows you to create interactive data stories, which provide a narrative flow by guiding users through a series of visualizations and insights.
Calculations and Expressions: Tableau offers a powerful calculation engine that allows you to create calculated fields, perform mathematical operations, apply logical conditions, and create complex formulas. Tableau's calculation capabilities enable advanced data analysis and custom calculations to derive insights from the data.
Advanced Analytics: Tableau provides advanced analytics features such as forecasting, trend lines, clustering, and statistical modeling. These capabilities allow users to uncover patterns, identify trends, and make predictions based on historical data.
Mapping and Geospatial Analysis: Tableau has robust mapping capabilities that enable the creation of interactive maps and geospatial visualizations. Users can plot data points on maps, perform geocoding, apply spatial filters, and perform spatial analysis to gain insights based on geographic data.
Data Preparation and Cleansing: Tableau offers data preparation features to clean and transform data directly within the tool. It provides tools for data cleansing, reshaping, splitting, and combining data, making it easier to prepare data for analysis without the need for external data preparation tools.
Collaboration and Sharing: Tableau allows users to collaborate by sharing dashboards, visualizations, and workbooks with others. It provides options to publish dashboards to Tableau Server or Tableau Public, enabling secure access and collaboration within an organization or sharing visualizations with the wider public.
Mobile and Web Accessibility: Tableau provides responsive design options, allowing dashboards and visualizations to be accessed and viewed on various devices, including desktops, tablets, and mobile phones. The Tableau Mobile app enables users to interact with and explore data on the go.

These are just some of the key features that make Tableau a popular choice for data visualization and analysis. Tableau's user-friendly interface, powerful analytics capabilities, and flexibility in data integration make it a versatile tool for individuals and organizations to gain insights from their data.

what are Tableau Filters? Explain in order ?

Data Connection and Integration: Tableau allows you to connect to various data sources, including databases, spreadsheets, cloud services, and web connectors. It supports direct connections to popular data platforms, and it provides options for data blending and joining to combine data from multiple sources.
Drag-and-Drop Interface: Tableau's intuitive drag-and-drop interface makes it easy to create interactive visualizations without the need for complex coding or scripting. You can simply drag fields onto the canvas to create charts, graphs, maps, and tables.
Visual Analytics: Tableau offers a wide range of visualizations, including bar charts, line charts, scatter plots, heat maps, treemaps, and more. It provides extensive customization options to control colors, labels, tooltips, and formatting to create visually appealing and meaningful visualizations.
Dashboards and Stories: Tableau allows you to create interactive dashboards by combining multiple visualizations into a single canvas. Dashboards provide an overview of data and enable users to interactively explore and analyze information. Additionally, Tableau allows you to create interactive data stories, which provide a narrative flow by guiding users through a series of visualizations and insights.
Calculations and Expressions: Tableau offers a powerful calculation engine that allows you to create calculated fields, perform mathematical operations, apply logical conditions, and create complex formulas. Tableau's calculation capabilities enable advanced data analysis and custom calculations to derive insights from the data.
Advanced Analytics: Tableau provides advanced analytics features such as forecasting, trend lines, clustering, and statistical modeling. These capabilities allow users to uncover patterns, identify trends, and make predictions based on historical data.
Mapping and Geospatial Analysis: Tableau has robust mapping capabilities that enable the creation of interactive maps and geospatial visualizations. Users can plot data points on maps, perform geocoding, apply spatial filters, and perform spatial analysis to gain insights based on geographic data.
Data Preparation and Cleansing: Tableau offers data preparation features to clean and transform data directly within the tool. It provides tools for data cleansing, reshaping, splitting, and combining data, making it easier to prepare data for analysis without the need for external data preparation tools.
Collaboration and Sharing: Tableau allows users to collaborate by sharing dashboards, visualizations, and workbooks with others. It provides options to publish dashboards to Tableau Server or Tableau Public, enabling secure access and collaboration within an organization or sharing visualizations with the wider public.
Mobile and Web Accessibility: Tableau provides responsive design options, allowing dashboards and visualizations to be accessed and viewed on various devices, including desktops, tablets, and mobile phones. The Tableau Mobile app enables users to interact with and explore data on the go.