- A
Create a Product dimension table by selecting distinct product attributes and assigning a surrogate key.
Product attributes should be in a separate dimension to avoid redundancy.
- B
Remove duplicate rows from the Product table and create a relationship to the fact table using a key.
Dimension tables should have unique rows and be linked via keys.
- C
Create a Date dimension table from the date column, including date attributes like year, quarter, month.
A date dimension is essential for time intelligence and should be created from the date column.
- D
Merge the Customer and Product dimensions into one table to reduce the number of tables.
Why wrong: Merging dimensions contradicts star schema principles and can cause data duplication.
- E
Keep all columns in a single table to avoid relationships.
Why wrong: A single table is not a star schema; it leads to redundancy and poor performance.
Quick Answer
The answer is to create a Date dimension table from the date column, including date attributes like year, quarter, and month. This is correct because transforming a flat table into a star schema in Power BI requires normalizing the single table into separate dimension and fact tables, which reduces data redundancy and improves query performance by enabling efficient filter propagation and compression. On the Microsoft Power BI Data Analyst PL-300 exam, this concept tests your understanding of dimensional modeling best practices, often appearing in case studies where you must identify which tables to create from a flat source. A common trap is forgetting to include a surrogate key in each dimension table, as natural keys can change and break relationships. Remember the memory tip: "Dim the lights, fact the numbers"—dimensions describe who, what, when, and where, while facts measure the sales.
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare 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 designing a data model for a sales analysis report. The source data includes a table with Sales, Product, Customer, and Date information in a single flat table. You need to transform this into a star schema with separate dimension tables. Which THREE actions should you take?
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 Product dimension table by selecting distinct product attributes and assigning a surrogate key.
Option A is correct because creating a Product dimension table by selecting distinct product attributes and assigning a surrogate key is a fundamental step in building a star schema. This process normalizes the flat table by separating product-related attributes into a dedicated dimension, which reduces redundancy and improves query performance in Power BI. The surrogate key (e.g., an integer identity column) ensures a stable, unique identifier for each product, independent of any changes in the source system's natural keys.
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.
- ✓
Create a Product dimension table by selecting distinct product attributes and assigning a surrogate key.
Why this is correct
Product attributes should be in a separate dimension to avoid redundancy.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Remove duplicate rows from the Product table and create a relationship to the fact table using a key.
Why this is correct
Dimension tables should have unique rows and be linked via keys.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create a Date dimension table from the date column, including date attributes like year, quarter, month.
Why this is correct
A date dimension is essential for time intelligence and should be created from the date column.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Merge the Customer and Product dimensions into one table to reduce the number of tables.
Why it's wrong here
Merging dimensions contradicts star schema principles and can cause data duplication.
- ✗
Keep all columns in a single table to avoid relationships.
Why it's wrong here
A single table is not a star schema; it leads to redundancy and poor performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think merging dimensions or keeping a single flat table simplifies the model, but the PL-300 exam specifically tests the requirement to normalize into a star schema for optimal performance and maintainability.
Detailed technical explanation
How to think about this question
In a star schema, dimension tables are denormalized and contain descriptive attributes, while the fact table stores quantitative measures and foreign keys referencing the dimensions. Power BI's VertiPaq engine compresses dimension tables more efficiently when they are small and have high cardinality columns, such as surrogate keys, which improves query response times. A real-world scenario where this matters is when a product's category changes over time; using a surrogate key in the dimension allows for slowly changing dimension (SCD) handling without breaking existing fact relationships.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this PL-300 question test?
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: Create a Product dimension table by selecting distinct product attributes and assigning a surrogate key. — Option A is correct because creating a Product dimension table by selecting distinct product attributes and assigning a surrogate key is a fundamental step in building a star schema. This process normalizes the flat table by separating product-related attributes into a dedicated dimension, which reduces redundancy and improves query performance in Power BI. The surrogate key (e.g., an integer identity column) ensures a stable, unique identifier for each product, independent of any changes in the source system's natural keys.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This PL-300 practice question is part of Courseiva's free Microsoft 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 PL-300 exam.
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