Tableau-Desktop-Found Connecting to and Preparing Data Practice Question
Exhibit
{
"connection_type": "Live",
"join_type": "Left Join",
"table_1": "Sales",
"table_2": "Targets",
"filter": "None",
"result": "Duplicated Sales Records"
}Refer to the exhibit. Why are the sales records being duplicated in the output?
⚠ Common exam trap
Candidates often blame incorrect aggregation formulas in calculated fields, failing to recognize that the root cause of the data inflation is a physical many-to-many join condition.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The join condition involves a many-to-many relationship.
The exhibit illustrates a common issue when joining tables at different granularities. A left join on 'Sales' and 'Targets' where multiple rows in 'Targets' exist for a single row in 'Sales' will cause the 'Sales' data to replicate for every match found in 'Targets'. Understanding this behavior is vital for maintaining data accuracy, as it demonstrates why relationships are often preferred over physical joins to avoid fan-traps and data duplication.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The join condition uses an incorrect join type.
Why it's wrong here
Changing the join type would not resolve the fundamental issue of grain mismatch. Whether you use an inner or left join, if the relationship between the tables is one-to-many, rows from the side with the lower grain will always be duplicated to match the higher grain side.
- ✓
The join condition involves a many-to-many relationship.
Why this is correct
A join operation physically merges rows. When a single row in the Sales table matches multiple rows in the Targets table, the result set duplicates the Sales row for each match. This is a classic 'fan-trap' issue caused by joining tables that are not at the same level of detail.
- ✗
The connection is set to Live instead of Extract.
Why it's wrong here
Connection mode has no impact on row duplication during a join. Row duplication is a product of the logical structure of the data and the join conditions applied. Switching to an extract would simply store the duplicated rows in a hyper file, failing to solve the underlying data model issue.
- ✗
The data contains null values in the join key.
Why it's wrong here
Null values in join keys generally result in dropped rows rather than duplicated rows in a standard left join. The issue here is related to the cardinality of the tables involved, specifically the one-to-many nature of the relationship, which causes the base table records to expand beyond their actual count.
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Last reviewed September 2026 · checked against the official Tableau (Salesforce) exam blueprint
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