The correct approach is to import the Sales, Stores, and Products tables, create a separate date table using the CALENDAR function, and establish relationships between the fact and dimension tables. This star schema design is essential because it minimizes model size by avoiding data duplication in the fact table while enabling efficient filtering by store, product category, and date. On the PL-300 exam, this scenario tests your understanding of dimensional modeling best practices and the necessity of a dedicated date table for time intelligence functions like SAMEPERIODLASTYEAR, which require a continuous, contiguous date range to calculate year-over-year sales growth accurately. A common trap is to use the SaleDate column directly from the Sales table for time calculations, which breaks DAX time intelligence and inflates model size due to missing dates. Remember the memory tip: “Fact for measures, dims for filters, date table for time shifts.”
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 a data analyst at a retail company. You are building a Power BI report to analyze sales performance across multiple stores. The source data comes from an Azure SQL Database that contains a table 'Sales' with columns: StoreID, ProductID, SaleDate, Quantity, and Amount. The database also has a 'Stores' table with StoreID and StoreName, and a 'Products' table with ProductID, ProductName, and Category. You need to create a data model that supports filtering by store, product category, and date, and also allows calculation of year-over-year sales growth. You want to minimize the model size and ensure optimal performance. The data volume is large (millions of rows). You must design the data model. What should you do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Import all tables as they are and create a single flat table by merging Sales, Stores, and Products in Power Query.
Why wrong: Flat table increases model size and reduces performance.
B
Import Sales, Stores, and Products tables, create a separate date table using CALENDAR, and establish relationships between Sales and dimension tables.
Star schema with date table improves performance and enables time intelligence.
C
Import Sales table only and create calculated columns for StoreName and ProductName using RELATED.
Why wrong: Requires relationships but misses date dimension.
D
Import Sales table and use the auto date/time feature for time intelligence.
Why wrong: Auto date/time creates hidden tables and is less flexible.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Import Sales, Stores, and Products tables, create a separate date table using CALENDAR, and establish relationships between Sales and dimension tables.
Option B is correct because it follows the star schema best practice: importing dimension tables (Stores, Products, a dedicated Date table) and the fact table (Sales) separately, then creating relationships. This minimizes model size by avoiding data duplication and enables efficient filtering by store, product category, and date. The separate date table is essential for accurate year-over-year calculations using DAX time intelligence functions like SAMEPERIODLASTYEAR, which require a continuous date range.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Import all tables as they are and create a single flat table by merging Sales, Stores, and Products in Power Query.
Why it's wrong here
Flat table increases model size and reduces performance.
✓
Import Sales, Stores, and Products tables, create a separate date table using CALENDAR, and establish relationships between Sales and dimension tables.
Why this is correct
Star schema with date table improves performance and enables time intelligence.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Import Sales table only and create calculated columns for StoreName and ProductName using RELATED.
Why it's wrong here
Requires relationships but misses date dimension.
✗
Import Sales table and use the auto date/time feature for time intelligence.
Why it's wrong here
Auto date/time creates hidden tables and is less flexible.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Option A (flat table) thinking it simplifies the model, not realizing that star schema design is essential for performance and compression in large datasets, and that Power BI's query folding can handle joins efficiently without merging.
Detailed technical explanation
How to think about this question
Under the hood, Power BI uses VertiPaq compression, which works best on high-cardinality columns in dimension tables and low-cardinality columns in fact tables; a star schema with separate date and dimension tables maximizes compression ratios. The dedicated date table must be marked as a date table in Power BI to enable time intelligence functions like TOTALYTD and SAMEPERIODLASTYEAR, which rely on a contiguous date range without gaps. In real-world scenarios, failing to create a separate date table can lead to incorrect year-over-year calculations when the fact table has missing dates or irregular intervals.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Import Sales, Stores, and Products tables, create a separate date table using CALENDAR, and establish relationships between Sales and dimension tables. — Option B is correct because it follows the star schema best practice: importing dimension tables (Stores, Products, a dedicated Date table) and the fact table (Sales) separately, then creating relationships. This minimizes model size by avoiding data duplication and enables efficient filtering by store, product category, and date. The separate date table is essential for accurate year-over-year calculations using DAX time intelligence functions like SAMEPERIODLASTYEAR, which require a continuous date range.
What should I do if I get this PL-300 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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