Tableau-Desktop-Found Connecting to and Preparing Data Practice Question
An analyst needs to combine two tables from a local PostgreSQL database. The tables share a common identifier, but one table contains transactional rows at a granular date level while the other contains monthly aggregate targets. What joining strategy best prevents metric inflation caused by data duplication?
⚠ Common exam trap
Candidates frequently select traditional joins for tables at different granularities, forgetting that standard physical joins cause row duplication and inflate additive metrics like sales or targets.
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
✓
Create a relationship between the two tables using the common identifier field.
Aggregating or properly relating tables at different levels of detail prevents duplication. While standard joins replicate matching rows and inflate additive metrics, Relationships handle mismatched levels dynamically by querying each table at its native grain before aggregation, preserving accurate totals without requiring manual pre-aggregation steps or complex custom SQL scripts in the workspace.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform an inner join on the common identifier to drop any unmatched target values.
Why it's wrong here
An inner join forces rows together at the lowest common denominator, causing the monthly targets to duplicate across every transactional row. This replication severely distorts additive metrics like sum of sales or target attainment calculations.
- ✓
Create a relationship between the two tables using the common identifier field.
Why this is correct
Relationships join tables at the visualisation layer using the common identifier, matching transactional rows to monthly targets without duplicating aggregate values across each date. This satisfies the stem's requirement to prevent metric inflation, unlike a physical join that would repeat monthly targets for every transaction row.
- ✗
Write a custom SQL query using a full outer join and multiple window functions.
Why it's wrong here
A full outer join still matches each transactional row to its monthly aggregate, replicating target values across every transaction and inflating totals; window functions cannot undo that row multiplication. It is tempting because outer joins preserve unmatched rows, which suits reconciling sparse datasets, but the scenario requires aggregating transactions to month before joining.
- ✗
Blend the data sources by setting up a primary and secondary data connection.
Why it's wrong here
Data blending computes aggregates separately in each data source and merges them in the view based on linking fields. However, blending is a legacy technique that operates as a left join and often results in asterisk values when linking granularities.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Tableau (Salesforce) exam blueprint
This Tableau-Desktop-Found practice question is part of Courseiva's free Tableau (Salesforce) certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Tableau-Desktop-Found exam.