PL-300 Model the data Practice Questions

Free PL-300 Model the data practice test questions

8 questions from this domain with answers and explanations - different from the samples on the main PL-300 page. A free account includes a fixed set of 20 questions for this exam; paid access unlocks the full bank.

1. Question 1Model the data

   You are designing a data model for a sales analysis solution. The model contains a Sales fact table and dimension tables for Product, Customer, and Date. Which schema design pattern does this represent?

   • ASnowflake schema
   • BFlat table design
   • CNormalized schema
   • DStar schemaCorrect
   ✓ Correct answer: D

   A star schema places a central fact table surrounded by independent dimension tables, each connected directly to the fact table by a relationship. With a Sales fact table linked to Product, Customer, and Date dimensions, the diagram radiates outward like a star, which minimizes joins and makes the model efficient and intuitive for analytical queries in Power BI.

   Why the other options are wrong

   • AA snowflake schema further normalizes the dimension tables into chains of related sub-tables (for example Product to Category to Subcategory), a layering that is not present in this single-level dimension design.
   • BA flat table design stores all attributes and facts together in one wide, denormalized table with no separate dimensions, which is the opposite of the multi-table structure described here.
   • CA normalized schema refers to the highly normalized relational designs used for transactional systems, which differ from the analytics-oriented dimensional model described in this scenario.
2. Question 2Model the data

   You need to create a DAX measure that calculates running total of sales from the beginning of the year to the current date in the filter context. Which DAX function should you use?

   • ACLOSINGBALANCEYEAR
   • BTOTALYTDCorrect
   • CPARALLELPERIOD
   • DPREVIOUSYEAR
   ✓ Correct answer: B

   TOTALYTD is a time intelligence function that accumulates a measure from the beginning of a calendar year to the current date in the filter context. It is specifically designed for year-to-date running totals. The function automatically handles date navigation within the defined date table, making it the most appropriate choice for a running total from January 1st through the current date in any year.

   Why the other options are wrong

   • ACLOSINGBALANCEYEAR is incorrect because it returns the value at the end of the year, not a running total from the beginning of the year.
   • CPARALLELPERIOD is incorrect because it shifts the filter context to a different period for comparison, not for calculating running totals.
   • DPREVIOUSYEAR is incorrect because it returns values from the previous year, not a running total from the current year's start.
3. Question 3Model the data

   Munson's Pickles and Preserves Farm has a model where the Budget table and Sales table share a relationship through a shared Date dimension. The analyst wants to compare budget vs. actuals in the same visual. However, the Budget table has monthly granularity while Sales has daily granularity. Which relationship type should be used between the Date table and the Budget table?

   • AMany-to-many between Date and BudgetCorrect
   • BOne-to-many from Date to Budget using the full date column
   • COne-to-many from Budget to Date
   • DOne-to-one between Date and Budget
   ✓ Correct answer: A

   When two fact tables share a dimension at different grains, here daily Sales versus monthly Budget, a single date key cannot establish a clean one-to-many link because each Budget month corresponds to many dates. Relating the Date table to Budget as many-to-many lets every daily date map to its corresponding monthly budget entry through the shared dimension, so both measures can be plotted together. This pattern is the standard Power BI approach for reconciling granularity mismatches between fact tables. It keeps the comparison unambiguous without forcing the budget data down to a daily grain.

   Why the other options are wrong

   • BA one-to-many from Date to Budget on the full date column would only match the few exact dates present in Budget, leaving most days unmapped and the comparison incomplete.
   • CA one-to-many from Budget to Date reverses the normal direction, since relationships flow from the dimension on the one side to fact tables on the many side, not from a fact table outward.
   • DA one-to-one relationship is wrong because Budget is a fact table with many rows per period rather than a dimension holding a single row per date.
4. Question 4Model the data

   You need to create a measure that counts the number of distinct customers who made purchases. Which DAX function should you use?

   • ACOUNTA(Sales[CustomerID])
   • BCOUNT(Sales[CustomerID])
   • CCOUNTROWS(Customer)
   • DDISTINCTCOUNT(Sales[CustomerID])Correct
   ✓ Correct answer: D

   The DISTINCTCOUNT function is purpose-built to count unique values within a column, automatically handling duplicate removal at the aggregation level. This is the most efficient and semantically correct approach for counting distinct customers, as it's optimized for cardinality calculations at query time.

   Why the other options are wrong

   • ACOUNTA(Sales[CustomerID]) is incorrect because COUNTA counts all non-blank cells including duplicates, so it would overcount customers who made multiple purchases.
   • BCOUNT(Sales[CustomerID]) is incorrect because COUNT counts numeric non-blank cells but also includes duplicates, missing the distinct requirement.
   • CCOUNTROWS(Customer) is incorrect because it counts all rows in the Customer table regardless of whether they have sales, which includes inactive customers.
5. Question 5Model the data

   A data model has a Fact_Orders table related to Dim_Customer (one-to-many) and Dim_Product (one-to-many). The analyst needs a measure that counts orders containing at least one product from the 'Electronics' category. Which DAX expression is correct?

   • AElectronics Orders = SUMX(Fact_Orders, IF(RELATED(Dim_Product[Category]) = "Electronics", 1, BLANK()))
   • BElectronics Orders = CALCULATE(COUNTROWS(Fact_Orders), KEEPFILTERS(Dim_Product[Category] = "Electronics"))
   • CElectronics Orders = CALCULATE(DISTINCTCOUNT(Fact_Orders[OrderID]), Dim_Product[Category] = "Electronics")Correct
   • DElectronics Orders = COUNTROWS(FILTER(ALL(Dim_Product), Dim_Product[Category] = "Electronics"))
   ✓ Correct answer: C

   Counting orders that contain at least one Electronics product means counting distinct order identifiers among the fact rows that join to Electronics products. CALCULATE applies the Electronics filter through the relationship to Dim_Product, and DISTINCTCOUNT on OrderID collapses multiple line items of the same order into a single count. This correctly yields the number of unique qualifying orders.

   Why the other options are wrong

   • ASUMX iterates the Fact_Orders rows and RELATED fetches each line's product category, so the expression adds one for every Electronics line item and an order containing three Electronics products is counted three times.
   • BCOUNTROWS(Fact_Orders) under the category filter counts qualifying fact rows, which are order lines rather than orders, and KEEPFILTERS only changes how the new filter combines with existing context, so multi-line orders are still counted more than once.
   • DCOUNTROWS over a filtered Dim_Product counts how many products sit in the Electronics category, and ALL discards the report's own filters as well, so the result describes the product catalogue rather than how many orders contain those products.
6. Question 6Model the dataSelect all that apply

   A consultant is reviewing the cross-filter direction configuration at Litware Inc. Which two actions should be performed to optimize the implementation? (Choose two.)

   • ADisable cross-filter direction monitoring
   • BFact tables
   • CContext transition
   • DDate tablesCorrect
   • ESynonyms for Q&ACorrect
   ✓ Correct answer: D, E

   Marking a table as an official date table gives cross-filtering and time intelligence functions a reliable calendar to evaluate against, and adding synonyms improves how Q&A parses natural-language questions about the model's columns and measures, both genuinely configurable actions. There is no cross-filter direction monitoring toggle to disable in Power BI. Fact tables describe a schema role rather than an action you perform. Context transition is DAX measure behavior triggered automatically by CALCULATE, not a setting you configure for cross-filter direction.

   Why the other options are wrong

   • ADisable cross-filter direction monitoring is incorrect because Power BI has no such monitoring toggle; cross-filter direction is a per-relationship property set to Single or Both, not something with a monitoring switch.
   • BFact tables is incorrect because a fact table is a schema role, the table holding transactional measures, not a configurable action for optimizing how filters propagate between tables.
   • CContext transition is incorrect because it is DAX behavior that CALCULATE triggers automatically when evaluating a measure inside a row context, not a relationship or model setting related to cross-filter direction.
7. Question 7Model the data

   Contoso Ltd is evaluating Q&A optimization. Q&A optimization can be used in conjunction with fact tables to enhance the overall solution.

   • TrueTrueCorrect
   • FalseFalse
   ✓ Correct answer: True

   Q&A answers a question by mapping its words onto model objects, and the objects it most needs to find are the measures over the fact table, because an aggregate is what most questions ask for. The Q&A setup pane is where that mapping is tuned: synonyms give a measure or column the words users actually type, Review questions shows the terms Q&A failed to resolve, and excluding staging tables stops it matching objects nobody should query. Data categories help too, since a column categorised as City produces map answers and a marked date table makes last quarter resolvable. The fact table supplies the numbers and the optimisation supplies the vocabulary that reaches them, which is why the two are configured together.

   Why the other options are wrong

   • FalseFalse is incorrect. The statement is true. Q&A optimization and fact tables are synergistic components that enhance Power BI's natural language analytics capabilities. Fact tables supply the measurable data and core DAX measures that Q&A queries analyze, while Q&A optimization configures synonyms, data categories, and other metadata to help the feature understand business terminology and user intent.
8. Question 8Model the data

   A report author wants end users to choose, from a single slicer, whether a bar chart's axis and a column's values switch between Product, Region, or Channel without building three separate visuals. The underlying model is a clean star schema. Which native Power BI modeling feature should you implement?

   • AA field parameter, which creates a table of selectable fields (and/or measures) that users pick from a slicer to dynamically swap what a visual displays.Correct
   • BA calculation group that contains one calculation item per dimension, so that selecting an item from a slicer switches which field the visual groups its axis by
   • CA what-if parameter generated with GENERATESERIES, which builds a numeric slicer that the user drags to swap out the visual's axis field dynamically
   • DA bookmark group bound to three separate buttons, where each button swaps the canvas to a pre-built visual for Product, Region, or Channel
   ✓ Correct answer: A

   Field parameters (New parameter > Fields) generate a special table that lists chosen columns and/or measures, with the function NAMEOF capturing each field reference. Placing that parameter on a slicer lets users switch the axis or values of a single visual among Product, Region, or Channel at runtime, exactly the requested swapping behavior, without duplicating visuals. This is a model-layer feature distinct from numeric what-if parameters.

   Why the other options are wrong

   • BCalculation groups switch calculation logic like time intelligence, not the dimension a visual's axis uses.
   • CA what-if parameter yields a numeric value for scenarios and cannot swap which column a visual displays.
   • DThree bookmarks and buttons require building separate visuals, which the single-slicer requirement rules out.

How Model the data is tested

This domain holds 188 of the 706 questions in the PL-300 bank, about 27%. The mix is 106 single-answer multiple choice, 39 multiple-response, 31 true/false, 5 hotspot, 4 ordering and 3 yes/no scenario, so it is worth practising the formats as well as the content.

Once you have a few attempts recorded, CertGrid scores every domain separately and points you at the weakest one, so you can drill Model the data on its own rather than re-running full-length mocks.

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