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 TWO of the following are best practices when designing star schemas in Power BI? (Select two.)
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
✓
Store numeric measures in fact tables.
Option A is correct because fact tables in a star schema are designed to store quantitative, numeric measures (e.g., sales amount, quantity) that can be aggregated. This aligns with the star schema principle of separating measures (facts) from descriptive context (dimensions), enabling efficient compression and fast aggregations in Power BI's VertiPaq engine.
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.
✓
Store numeric measures in fact tables.
Why this is correct
Fact tables contain the measurements.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use calculated columns in fact tables for row-level security.
Why it's wrong here
RLS is defined on tables, not via calculated columns.
✓
Place descriptive attributes in dimension tables.
Why this is correct
Dimensions provide context for filtering and grouping.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Include many columns in fact tables for filtering.
Why it's wrong here
Fact tables should be slim; filtering is done via dimensions.
✗
Normalize dimension tables to reduce redundancy.
Why it's wrong here
Star schema denormalizes dimensions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse normalization (Option E) as a best practice from transactional databases, but Power BI star schemas require denormalized dimensions for optimal performance, and they may also mistakenly think calculated columns in fact tables (Option B) are acceptable for RLS, ignoring the performance and design implications.
Detailed technical explanation
How to think about this question
Under the hood, Power BI's VertiPaq engine uses columnar compression and dictionary encoding, which works best on high-cardinality, low-repetition columns typical of dimension tables. Fact tables should remain narrow with only foreign keys and numeric measures to maximize compression ratios and minimize memory footprint. In real-world scenarios, adding a single text column to a fact table can increase model size by gigabytes due to poor compression, while storing the same attribute in a dimension table leverages referential integrity and efficient filtering.
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: Store numeric measures in fact tables. — Option A is correct because fact tables in a star schema are designed to store quantitative, numeric measures (e.g., sales amount, quantity) that can be aggregated. This aligns with the star schema principle of separating measures (facts) from descriptive context (dimensions), enabling efficient compression and fast aggregations in Power BI's VertiPaq engine.
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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