The answer is that dimension tables should be denormalized, a separate date table is required, and fact tables should contain only foreign keys and measures. Denormalization in dimension tables reduces the number of joins and improves query performance by flattening hierarchies like time or geography into a single table, which is critical for star schema design considerations in Power BI. A dedicated date table is mandatory because DAX time intelligence functions such as TOTALYTD require a continuous, gap-free date range to calculate correctly across all granularities. On the PL-300 exam, this tests your understanding of how star schemas optimize data models for both speed and DAX functionality—a common trap is assuming you can use a date column directly from a fact table. Remember the mnemonic: “Dims are flat, facts are thin, dates must be a table within.”
PL-300 Model the data Practice Question
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.
Which THREE factors should you consider when designing a star schema in Power BI?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A separate date table should be created for time intelligence.
A separate date table is required for time intelligence functions in Power BI because DAX time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR) rely on a continuous, contiguous date range with no gaps. Power BI automatically marks a table as a date table only if it contains a complete set of dates from the earliest to the latest transaction, enabling functions like DATEADD and DATESBETWEEN to work correctly across all granularities.
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.
✓
A separate date table should be created for time intelligence.
Why this is correct
A dedicated date table enables time-based calculations.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use natural keys instead of surrogate keys in dimension tables.
Why it's wrong here
Surrogate keys are preferred for stability and performance.
✓
Fact tables should contain only foreign keys and numeric measures.
Why this is correct
This minimizes table size and improves aggregation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a snowflake schema to reduce data redundancy.
Why it's wrong here
Snowflake schemas are generally not recommended in Power BI due to performance overhead.
✓
Dimension tables should be denormalized.
Why this is correct
Denormalized dimensions reduce the number of tables and improve query performance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the theoretical normalization benefits of a snowflake schema (reducing redundancy) with the practical performance requirements of Power BI, where denormalization and surrogate keys are essential for optimal query execution and time intelligence calculations.
Detailed technical explanation
How to think about this question
Under the hood, Power BI's VertiPaq engine stores data in columnar compression, and denormalized dimension tables reduce the number of table relationships and join paths, allowing faster filter propagation and aggregation. In a real-world scenario, using a snowflake schema with separate tables for, say, Product Category and Product Subcategory forces multiple cross-table filter operations, which can increase query time by 30–50% compared to a single denormalized Product dimension. Additionally, surrogate keys in dimension tables enable stable row-level security (RLS) mappings and avoid key collisions when source systems recycle natural keys.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Model the data — This question tests Model the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A separate date table should be created for time intelligence. — A separate date table is required for time intelligence functions in Power BI because DAX time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR) rely on a continuous, contiguous date range with no gaps. Power BI automatically marks a table as a date table only if it contains a complete set of dates from the earliest to the latest transaction, enabling functions like DATEADD and DATESBETWEEN to work correctly across all granularities.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE factors should you consider when designing a star schema for a Power BI semantic model? (Select three.)
hard
✓ A.Fact tables should contain measures and foreign keys to dimension tables.
✓ B.Dimension tables should contain descriptive attributes and be denormalized.
C.Fact tables should be normalized to reduce data duplication.
D.Use calculated columns in dimension tables to derive new attributes.
✓ E.Avoid creating many-to-many relationships between dimensions.
Why A: Options A, C, and D are correct. Star schema design emphasizes dimension tables with descriptive attributes, fact tables with measures and foreign keys, and avoiding many-to-many relationships. Option B is wrong because normalization is for OLTP, not for analysis. Option E is wrong because calculated columns are generally not recommended for dimension tables.
Last reviewed: Jun 24, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.