Question 837 of 966

Model the data →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to create a date dimension table in the SQL Server data warehouse and use that table in the Power BI model for date filtering, ensuring the date column is indexed. This works because DirectQuery performance is heavily dependent on the efficiency of the queries sent to the source; a dedicated, indexed date dimension allows the SQL Server engine to perform a seek operation rather than a full scan of the billions of rows in the Transactions table, dramatically reducing I/O and query time. On the PL-300 exam, this scenario tests your understanding of star schema design and DirectQuery limitations—a common trap is assuming you can use calculated tables or DAX date functions, but these are either unsupported in DirectQuery or create expensive row-level computations. Remember the memory tip: “Date dim, not date trim”—always trim your query scope with a pre-built, indexed dimension table at the source, not by trimming rows in Power BI.

PL-300 Model the data Practice Question

This PL-300 practice question tests your understanding of model the data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data analyst for a financial services firm. You have a Power BI semantic model that uses DirectQuery mode against a SQL Server data warehouse. The model includes a large fact table named Transactions with billions of rows. Users are complaining that the report is slow when filtering by date range. You need to improve the performance of date-related queries without changing the data source structure. You cannot switch to Import mode due to data volume constraints. What should you do?

Question 1hardmultiple choice

Full question →

A
Switch to Import mode for the Transactions table and schedule frequent refreshes.
Why wrong: Data volume is too large for Import mode.
B
Create a date dimension table in the SQL Server data warehouse and use that table in the Power BI model for date filtering. Ensure the date column is indexed.
A date dimension table reduces the cardinality of date columns and improves query performance.
C
Add an index on the date column in the fact table and use the date column directly in visuals.
Why wrong: Indexing helps but without a date dimension, performance improvements are limited.
D
Create a calculated table in Power BI using DAX to generate a distinct list of dates from the Transactions table.
Why wrong: Calculated tables are not supported in DirectQuery mode.

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 in the SQL Server data warehouse and use that table in the Power BI model for date filtering. Ensure the date column is indexed.

Option A is correct: creating a date dimension table in the data warehouse and using it for filtering reduces the number of rows scanned. Option B would increase model size and maintenance. Option C is not possible because calculated tables are not supported in DirectQuery. Option D would not help performance.

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.

✗
Switch to Import mode for the Transactions table and schedule frequent refreshes.
Why it's wrong here
Data volume is too large for Import mode.
✓
Create a date dimension table in the SQL Server data warehouse and use that table in the Power BI model for date filtering. Ensure the date column is indexed.
Why this is correct
A date dimension table reduces the cardinality of date columns and improves query performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add an index on the date column in the fact table and use the date column directly in visuals.
Why it's wrong here
Indexing helps but without a date dimension, performance improvements are limited.
✗
Create a calculated table in Power BI using DAX to generate a distinct list of dates from the Transactions table.
Why it's wrong here
Calculated tables are not supported in DirectQuery mode.

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?

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: Create a date dimension table in the SQL Server data warehouse and use that table in the Power BI model for date filtering. Ensure the date column is indexed. — Option A is correct: creating a date dimension table in the data warehouse and using it for filtering reduces the number of rows scanned. Option B would increase model size and maintenance. Option C is not possible because calculated tables are not supported in DirectQuery. Option D would not help performance.

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.

About these practice questions

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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 data modeler for a logistics company. The company uses Power BI to analyze shipment data. The source system is a SQL Server database with tables: Shipments (ShipmentID, ShipDate, CustomerID, Weight, Cost, CarrierID), Customers (CustomerID, CustomerName, Region), Carriers (CarrierID, CarrierName, Mode). The database is updated in real-time. You need to build a semantic model that supports near real-time reporting with maximum data freshness while maintaining good query performance. The model should allow users to filter by customer region and carrier mode, and calculate total cost and weight per month. What is the best approach?

hard

A.Import the three tables into Power BI and create a star schema with relationships.
B.Create a single view in SQL Server that joins all tables and use DirectQuery on that view.
✓ C.Use DirectQuery on the three tables and create a date table in the model.
D.Use a composite model: import Customers and Carriers, and use DirectQuery on Shipments.

Why C: Option C is correct because using DirectQuery (or dual storage) with a date table allows real-time data access and supports time intelligence. Option A is wrong because DirectQuery on a single flat table loses relationships. Option B is wrong because importing into a star schema does not support real-time. Option D is wrong because composite model is not needed for this scenario.

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