Quick Answer
The answer is many-to-one (*:1) cardinality. This is correct because the relationship cardinality between fact and date table in a star schema is defined by the grain of each table: the Sales fact table contains many rows per date (multiple orders on the same day), while the Date dimension table contains only one row per unique date. This creates a classic *:1 relationship where the many side (Sales) filters down to the one side (Date). On the PL-300 exam, this scenario tests your understanding of dimensional modeling fundamentals—a common trap is choosing one-to-many (1:*), which reverses the filter direction and breaks the model. Remember that Power BI always defines cardinality from the perspective of the table on the "many" side pointing to the "one" side. A useful memory tip: "Facts are many, dates are one—think *:1 for the star to run."
This PL-300 practice question tests your understanding of model the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are modeling data for a sales analysis. You have a fact table 'Sales' and a dimension table 'Date'. You need to create a relationship between 'Sales[OrderDate]' and 'Date[Date]'. The 'Date' table contains dates from January 1, 2020 to December 31, 2025. The 'Sales' table contains orders from January 1, 2021 to June 30, 2024. Which relationship cardinality should you use?
Many-to-many (*:*)
Why wrong: Many-to-many is not required because Date table has unique dates.
One-to-many (1:*)
Why wrong: One-to-many would mean one sales row for many dates, which is incorrect.
Many-to-one (*:1)
Many sales rows for one date, and the Date table has unique dates.
One-to-one (1:1)
Why wrong: Sales can have many rows per date, so this is not one-to-one.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Many-to-one (*:1)
The correct cardinality is Many-to-one (*:1) because the 'Sales' table contains many rows for each date (multiple orders can occur on the same date), while the 'Date' table has unique dates. This creates a classic star-schema relationship where the many side (Sales) points to the one side (Date). In Power BI, the cardinality is defined from the perspective of the filter propagation direction, so *:1 means many rows in Sales match one row in Date.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Many-to-many (*:*)
Why it's wrong here
Many-to-many is not required because Date table has unique dates.
One-to-many (1:*)
Why it's wrong here
One-to-many would mean one sales row for many dates, which is incorrect.
Many-to-one (*:1)
Why this is correct
Many sales rows for one date, and the Date table has unique dates.
Related concept
Read the scenario before looking for a memorised answer.
One-to-one (1:1)
Why it's wrong here
Sales can have many rows per date, so this is not one-to-one.
Common exam traps
The trap here is that candidates often confuse the direction of the cardinality notation (1:* vs *:1) and incorrectly select One-to-many (1:*) because they think the 'one' side should be the dimension table, but Power BI's cardinality is defined from the filter context perspective, not the table roles.
Detailed technical explanation
In Power BI, the cardinality setting determines how filters propagate across relationships. With *:1, filtering from the Date table (the '1' side) will automatically filter all matching rows in Sales, enabling time-intelligence calculations like YTD or MTD. A subtle behavior is that if the Date table contains dates not present in Sales (e.g., future dates), the relationship still works, but those dates will show blank measures unless you use a cross-filter direction or a disconnected table.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Model the data — This question tests Model the data — Read the scenario before looking for a memorised answer..
The correct answer is: Many-to-one (*:1) — The correct cardinality is Many-to-one (*:1) because the 'Sales' table contains many rows for each date (multiple orders can occur on the same date), while the 'Date' table has unique dates. This creates a classic star-schema relationship where the many side (Sales) points to the one side (Date). In Power BI, the cardinality is defined from the perspective of the filter propagation direction, so *:1 means many rows in Sales match one row in Date.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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