Connect To and Transform Data24% of the exam
Relationships, joins, unions and data source filters
Combine tables in the right layer (relationships and multi-fact models in the logical layer, joins and unions in the physical layer), validate a relationship model for missing or repeated values, and choose between data source filters and extract filters.
Prepare data for analysis: Assess data quality (completeness, consistency, accuracy); Perform cleaning operations; Organize data fields into folders; Use multiple data sources (establish relationships, create joins, union tables); Prepare data by using Data Interpreter, pivot, and split; Create extract and data source filters.
~7 min read · 7 practice questions ·
What the exam expects
- Choose a relationship, join or union from what the analysis needs (more columns, more rows, or tables at different levels of detail), including a multi-fact relationship model when tables would form a cycle.
- Build joins with join clauses and calculations, and unions by name, by field order or by wildcard, merging mismatched fields.
- Assess completeness and accuracy of a relationship model with Count fields, View Data and Show Empty Rows, and set Performance Options safely.
- Create data source filters and extract filters, and know which applies where.
- Data Interpreter, pivot, split, data quality checks and Data pane folders are taught in Cleaning data with Data Interpreter, pivot and split; the basics are in the Desktop Foundations units Relationships versus joins in the data model and Join types and unions in the physical layer.
Key terms:
- Logical layer: the Relationships canvas, where logical tables stay separate at their own level of detail.
- Physical layer: the Join/Union canvas inside a logical table, where tables merge into one flat table.
- Multi-fact relationship model: a data model with more than one base table, linked through shared tables (2024.2 and later).
- Data source filter: a filter on the data source that every query of it applies.
- Extract filter: a filter that limits which rows are pulled into an extract.
Key facts
Relationship join type
- Detail
- None to set; Tableau picks per query
Relationship limits
- Detail
- Same data type; no geographic fields; no cycles
Multi-fact models
- Detail
- Version 2024.2 and later
Performance Options defaults
- Detail
- Many-to-Many; Some Records Match
Dimension-only view
- Detail
- Inner joins; unmatched domain hidden
Split fields
- Detail
- Can't be join keys
Headerless union
- Detail
- Generate field names automatically
Edit Data Source Filters OK
- Detail
- Promotes every listed global filter
Extract filters, multi-fact
- Detail
- Applied per table
Sources for this section (6)
- The Tableau Data Modelhelp.tableau.com (opens in a new tab)
- Relate Your Datahelp.tableau.com (opens in a new tab)
- Optimize Relationship Queries Using Performance Optionshelp.tableau.com (opens in a new tab)
- Split a Field into Multiple Fieldshelp.tableau.com (opens in a new tab)
- Union Your Datahelp.tableau.com (opens in a new tab)
- Filter Data from Data Sourceshelp.tableau.com (opens in a new tab)
- Why can setting Cardinality to One distort totals?
- When should you choose a join over a relationship?
- Why can a data source filter slow a multi-table data source?
- How are unrelated dimensions from two base tables combined in one view?
The questions are free to read. Answers with their sources and self-grading are included with the pack.
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