The correct approach is to create a separate Time dimension table with a one-to-many relationship to the fact table. This follows star schema best practices, keeping the fact table lean by avoiding calculated columns for hour extraction while enabling efficient filtering and grouping by hour across multiple fact tables. On the Microsoft Power BI Data Analyst PL-300 exam, this tests your understanding of dimensional modeling—specifically how to handle time-based analysis without bloating transactional data. A common trap is adding calculated columns like HOUR(TransactionDate) directly to the fact table, which works but violates star schema principles and reduces performance when scaling. Instead, the separate Time dimension table can include columns like Hour, HourBucket, or PeriodOfDay, allowing intuitive slicers and drill-downs. Memory tip: think of the Time dimension as a “clock” that sits beside your data, not inside it—separate tables keep your model clean and your queries fast.
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 Power BI solution for a retail company. The data includes point-of-sale transactions with columns: TransactionID, StoreID, ProductID, Quantity, SalesAmount, TransactionDate. The company wants to analyze sales by hour of day. What is the best way to prepare the time dimension?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a separate Time dimension table with a one-to-many relationship to the fact table
Option D is correct because creating a separate Time dimension table with a one-to-many relationship to the fact table follows the star schema best practice for Power BI. This approach allows efficient filtering and grouping by hour without bloating the fact table with calculated columns, and it supports time-based analysis across multiple fact tables if needed. The Time dimension can include columns like Hour, HourBucket, or PeriodOfDay, enabling intuitive slicers and drill-downs.
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.
✗
Add a calculated column extracting hour from TransactionDate in the fact table
Why it's wrong here
Managed as a degenerate dimension, less flexible.
✗
Create a date dimension only and ignore time
Why it's wrong here
Loses hour analysis capability.
✗
Use DAX measures to extract hour on the fly
Why it's wrong here
Less efficient and not reusable.
✓
Create a separate Time dimension table with a one-to-many relationship to the fact table
Why this is correct
A time dimension allows consistent time analysis.
Clue confirmation
The clue word "best" in the question point toward this answer.
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 often think extracting the hour via a calculated column (Option A) is simpler and sufficient, but they overlook the performance and scalability benefits of a proper star schema with a separate Time dimension, which is a core concept tested in PL-300.
Detailed technical explanation
How to think about this question
In Power BI, a Time dimension table typically contains a single column of time values (e.g., 00:00:00 to 23:59:59) with a one-to-many relationship to a datetime column in the fact table, often using a surrogate key like TimeID. Under the hood, Power BI compresses relationships using in-memory columnar storage (VertiPaq), so a separate Time table reduces cardinality in the fact table and improves query performance for time-based aggregations. A real-world scenario is a retail chain with stores in different time zones, where a Time dimension with offset handling enables consistent hourly analysis across regions.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Create a separate Time dimension table with a one-to-many relationship to the fact table — Option D is correct because creating a separate Time dimension table with a one-to-many relationship to the fact table follows the star schema best practice for Power BI. This approach allows efficient filtering and grouping by hour without bloating the fact table with calculated columns, and it supports time-based analysis across multiple fact tables if needed. The Time dimension can include columns like Hour, HourBucket, or PeriodOfDay, enabling intuitive slicers and drill-downs.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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