Question 305 of 966

Visualize and analyze the data →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to create a date dimension table and build a star schema with Sales as the fact table and Products, Customers, and Date as dimensions, then use TOTALYTD for the running total and TOPN for the top products. This is correct because a star schema design for Power BI optimizes query performance by separating transactional data (facts) from descriptive attributes (dimensions), enabling efficient aggregations and fast report loading. On the PL-300 exam, this scenario tests your understanding of dimensional modeling and DAX best practices, often appearing as a trap where candidates choose a flat table or DirectQuery, both of which degrade performance for measures like running totals. A common memory tip is “facts are skinny, dimensions are wide”—fact tables store numeric measures and foreign keys, while dimension tables hold descriptive text for slicing. Remember, for time intelligence like running totals, always use a dedicated date table marked as such, and for top N visuals, rely on TOPN in a measure rather than filters.

PL-300 Visualize and analyze the data Practice Question

This PL-300 practice question tests your understanding of visualize and analyze 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 a Power BI analyst at a retail company. The company has a SQL Server data warehouse with tables: 'Sales' (SalesID, ProductID, CustomerID, SalesDate, Amount, Quantity), 'Products' (ProductID, ProductName, Category, UnitPrice), 'Customers' (CustomerID, CustomerName, Region, Segment). The data warehouse is refreshed nightly. You need to build a Power BI solution that meets the following requirements: - The report must be published to the Power BI service and support scheduled refresh. - Users must be able to analyze sales by product category and customer region. - The data model should follow a star schema design. - A measure must calculate the running total of sales by date. - The report must include a visual that shows the top 5 products by sales amount for the current year. - Performance is critical; the report should load quickly.

You have imported the data into Power BI Desktop. Which of the following actions should you take to meet the requirements?

Question 1hardmultiple choice

Full question →

A
Create a date dimension table. Build a star schema with Sales as fact and Products, Customers, and Date as dimensions. Use TOTALYTD for running total and TOPN for top products.
This follows star schema best practices and uses efficient DAX functions.
B
Import all tables. Create a calculated column for running total using FILTER and EARLIER. Use a slicer to filter by current year and sort a bar chart by sales.
Why wrong: Calculated columns for running total are less efficient than measures.
C
Use DirectQuery mode to connect to the data warehouse. Write a SQL query for each visual to ensure real-time data. Use a SQL window function for running total.
Why wrong: DirectQuery would be slower for aggregated measures and complex calculations.
D
Create a single flat table by merging all tables in Power Query. Use a measure for running total with EARLIER function. Create a Top N filter on a table visual.
Why wrong: Flat tables are not star schema and EARLIER is inefficient for running totals.

Full breakdown with real-world context →

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 date dimension table. Build a star schema with Sales as fact and Products, Customers, and Date as dimensions. Use TOTALYTD for running total and TOPN for top products.

Option B is correct because creating a date dimension, using a star schema, and using DAX measures (including TOTALYTD for running total and TOPN for top products) is the best practice. Option A is wrong because using a single flat table would create a poor model and slow performance. Option C is wrong because DirectQuery would be slower for aggregated measures like running total. Option D is wrong because using calculated columns for running total is less efficient than measures.

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 date dimension table. Build a star schema with Sales as fact and Products, Customers, and Date as dimensions. Use TOTALYTD for running total and TOPN for top products.
Why this is correct
This follows star schema best practices and uses efficient DAX functions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Import all tables. Create a calculated column for running total using FILTER and EARLIER. Use a slicer to filter by current year and sort a bar chart by sales.
Why it's wrong here
Calculated columns for running total are less efficient than measures.
✗
Use DirectQuery mode to connect to the data warehouse. Write a SQL query for each visual to ensure real-time data. Use a SQL window function for running total.
Why it's wrong here
DirectQuery would be slower for aggregated measures and complex calculations.
✗
Create a single flat table by merging all tables in Power Query. Use a measure for running total with EARLIER function. Create a Top N filter on a table visual.
Why it's wrong here
Flat tables are not star schema and EARLIER is inefficient for running totals.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 PL-300 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this PL-300 question test?

Visualize and analyze the data — This question tests Visualize and analyze 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 date dimension table. Build a star schema with Sales as fact and Products, Customers, and Date as dimensions. Use TOTALYTD for running total and TOPN for top products. — Option B is correct because creating a date dimension, using a star schema, and using DAX measures (including TOTALYTD for running total and TOPN for top products) is the best practice. Option A is wrong because using a single flat table would create a poor model and slow performance. Option C is wrong because DirectQuery would be slower for aggregated measures like running total. Option D is wrong because using calculated columns for running total is less efficient than measures.

What should I do if I get this PL-300 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Same concept, more angles

1 more ways this is tested on PL-300

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. You are a Power BI data analyst at a global retail company. Your organization uses a SQL Server data warehouse to store sales transactions, product inventory, and customer demographics. The data warehouse is updated nightly. You have been tasked with building a Power BI report to analyze sales performance across different regions and product categories. The report must meet the following requirements: - Allow users to filter data by date, region, and product category. - Provide drill-down from region to store level. - Display key metrics such as total sales, sales growth compared to the previous year, and profit margin. - Ensure that the report loads quickly even when users apply multiple filters. - Users must be able to export the underlying data to Excel for further analysis. - The report should be accessible on mobile devices with a responsive layout. - You need to implement row-level security so that regional managers can only see data for their own region. - The dataset must support scheduled refresh. You have imported the necessary tables from the data warehouse using Import mode. You created a date dimension table using DAX and marked it as a date table. You also created a separate table for regions with a column 'RegionManagerEmail' that maps each region to the manager's email address. You plan to use RLS by creating a role that filters the region table based on the user's email address. After publishing the report to the Power BI service, you notice that the report takes a long time to load when users select a large date range. Additionally, the profit margin measure, which is calculated as DIVIDE(SUM(Sales[Profit]), SUM(Sales[Revenue])), returns blank for some rows even though both Profit and Revenue have values. You also receive feedback that the mobile layout is not optimized; the visuals are too small and the slicers are not accessible. You need to address these issues. What should you do?

hard

A.Implement incremental refresh on the fact table and create a separate measure for profit margin using COALESCE.
B.Change the storage mode to DirectQuery for the fact table and use the Power BI Desktop's 'Optimize for mobile' option.
✓ C.Create aggregations on the fact table in the data source to reduce data volume, verify relationships for the profit margin measure, and use the Power BI mobile layout view to optimize visuals and slicers.
D.Increase the capacity of the Power BI service to Premium Per User and enable large dataset storage.

Why C: Option C is correct because creating aggregations on the fact table in the data source (e.g., pre-aggregated daily sales) reduces the amount of data loaded into the model, improving load times. The profit margin issue is likely due to a missing relationship or filter context; verifying relationships and using DIVIDE with an alternate result will fix blanks. For mobile layout, using the mobile layout view to resize visuals and place slicers at the top improves accessibility. Option A is wrong because increasing RAM is a temporary fix and does not address the root cause. Option B is wrong because incremental refresh can help with load times but does not fix the profit margin issue or mobile layout. Option D is wrong because changing to DirectQuery might introduce performance issues and does not address the profit margin or mobile layout.

Last reviewed: Jun 21, 2026

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