Tableau Data Analyst Advanced Practice Exam: Hard Questions 2025

A retail analytics team publishes a dashboard that combines a very large Orders table (hundreds of millions of rows) from a cloud data warehouse with a small Promotions table stored as a local Excel file. The workbook uses an extract for performance. After publishing, users report that promotion attributes sometimes appear blank and row counts fluctuate between refreshes. The team also notices the extract refresh completes without errors. What is the most likely root cause and best fix?

An analyst builds a model where Sales (fact) is related to both Customers and Products in the logical layer. Customers and Products each have multiple records per key due to slowly changing attributes (Type 2), and the analyst wants the latest attributes by default without duplicating Sales. The dashboard must allow filtering by current Customer Segment and current Product Category while keeping historical sales totals accurate. What is the best approach?

A finance dashboard shows Year-over-Year (YoY) % change for monthly revenue. The analyst uses a table calculation: (SUM([Revenue]) - LOOKUP(SUM([Revenue]), -12)) / LOOKUP(SUM([Revenue]), -12). Users complain the YoY values change unexpectedly when they add a dimension (Region) to the view or filter to a subset of products. The analyst needs YoY to remain correct per month per region and to respect product filters. What should the analyst do?

A dashboard uses a parameter to switch between measures (Sales, Profit, Margin) and a separate parameter to switch between time grains (Day, Week, Month). Performance is poor because the workbook contains many calculated fields with repeated logic and multiple sheets duplicating the same structures. The analyst wants to simplify maintenance and improve performance while keeping the dynamic behavior. Which design is best?

An analyst is troubleshooting incorrect distinct counts of Customers when slicing by Campaign. The data model uses a relationship between Fact_Interactions and Dim_Customers, and another relationship between Fact_Interactions and Dim_Campaigns. Some interactions have multiple campaigns (many-to-many via a bridge table in the warehouse), but the Tableau model currently relates interactions directly to Dim_Campaigns on InteractionId. Distinct Customers by Campaign is inflated. What is the best fix in Tableau’s data model?

A published dashboard contains a map with point locations for customers and a polygon layer for sales territories. Users report that some customers appear in the wrong territory after the analyst switched the customer geocoding from Postal Code to Latitude/Longitude fields. The analyst suspects the issue is due to geographic role and aggregation behavior. What is the best troubleshooting and resolution approach?

A stakeholder requests a KPI tile that shows current month Sales and colors it red if it is below target. The target varies by Region and changes quarterly. The analyst initially hard-coded the target into a calculated field, but it’s now unmaintainable. The solution must be auditable and allow targets to be updated without editing the workbook. What should the analyst implement?

An executive dashboard uses several sheets with heavy calculations and high-cardinality filters (Customer, SKU). Users experience long load times and frequent "spinning" when interacting with filters. The analyst wants to optimize performance without changing the underlying database. Which combination of changes is most effective and aligned with Tableau best practices?

A workbook uses row-level security (RLS) based on a User-to-Region entitlement table. In Desktop, the analyst validates security using 'View Data' and it looks correct. After publishing, some users can see data from regions they should not access. The data source is a published extract that refreshes nightly, and the entitlement table is updated throughout the day. What is the most likely issue and best remediation?

A team needs to share an interactive dashboard broadly across the organization. Some consumers are not licensed for full Tableau access but must see up-to-date data and use basic interactivity (filtering, tooltips). The dashboard contains sensitive fields that should never be exposed, even through 'Download data'. What is the best approach to meet these requirements?