Question 675 of 966

Prepare the data →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to import all tables into Power BI’s in-memory VertiPaq engine, create a separate date table using CALENDAR, and establish relationships with a bridge table for the many-to-many link between quarterly earnings and daily dates. This design star schema from multiple data sources maximizes query performance for the 1.3 million row CSV while enabling accurate time intelligence and cross-filtering by sector, company, and date range. On the PL-300 exam, this scenario tests your understanding of star schema fundamentals and the critical distinction between import mode (optimal for large fact tables) and DirectQuery. A common trap is attempting to relate the earnings table directly to the date table, which would cause incorrect aggregation; the bridge table resolves this without performance degradation. Memory tip: “Bridge the gaps, star the facts” — always use a bridge table for non-granular date relationships in a star schema.

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 a Power BI developer for a financial services company. You are preparing data from multiple sources: a CSV file containing daily stock prices (ticker, date, close_price), a SQL Server database with company information (ticker, company_name, sector), and an Excel file with quarterly earnings data (ticker, quarter, earnings_per_share). The CSV file has 5 years of daily data (approx 1.3 million rows). The SQL Server table has 5000 rows. The Excel file has 20,000 rows. You need to create a data model that allows users to filter by sector, company, and date range, and to calculate moving averages of stock prices and compare earnings over time. Performance is critical. You must decide the best approach to combine and model this data. What should you do?

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.

Question 1hardmultiple choice

Full question →

A
Use DirectQuery for StockPrices (CSV) and import the other tables.
Why wrong: CSV cannot be used with DirectQuery; also performance would suffer.
B
Import only StockPrices and Company, and use the auto date/time feature; ignore Earnings data.
Why wrong: Ignores required data and auto date/time is less efficient.
C
Import all tables, create a date table with CALENDAR, and establish relationships: StockPrices[Date] -> DateTable[Date], StockPrices[Ticker] -> Company[Ticker], Earnings[Ticker] -> Company[Ticker], and create a many-to-many relationship between Earnings and DateTable using a bridge table.
Star schema with proper relationships optimizes performance.
D
Import all tables, then in Power Query merge StockPrices with Company and Earnings into a single flat table using left outer joins.
Why wrong: Flat table increases model size and slows performance.

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

✓

Import all tables, create a date table with CALENDAR, and establish relationships: StockPrices[Date] -> DateTable[Date], StockPrices[Ticker] -> Company[Ticker], Earnings[Ticker] -> Company[Ticker], and create a many-to-many relationship between Earnings and DateTable using a bridge table.

Option C is correct because importing all tables into the in-memory VertiPaq engine ensures optimal performance for large datasets (1.3M rows) and complex calculations like moving averages. Creating a separate date table with CALENDAR enables proper time intelligence, while the bridge table resolves the many-to-many relationship between quarterly earnings and daily dates, allowing accurate filtering by sector, company, and date range without performance degradation.

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.

✗
Use DirectQuery for StockPrices (CSV) and import the other tables.
Why it's wrong here
CSV cannot be used with DirectQuery; also performance would suffer.
✗
Import only StockPrices and Company, and use the auto date/time feature; ignore Earnings data.
Why it's wrong here
Ignores required data and auto date/time is less efficient.
✓
Import all tables, create a date table with CALENDAR, and establish relationships: StockPrices[Date] -> DateTable[Date], StockPrices[Ticker] -> Company[Ticker], Earnings[Ticker] -> Company[Ticker], and create a many-to-many relationship between Earnings and DateTable using a bridge table.
Why this is correct
Star schema with proper relationships optimizes performance.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Import all tables, then in Power Query merge StockPrices with Company and Earnings into a single flat table using left outer joins.
Why it's wrong here
Flat table increases model size and slows performance.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose Option D (flat table) thinking it simplifies the model, but they overlook the severe performance hit from data duplication and the inability to use star schema optimizations for time intelligence and filtering.

Detailed technical explanation

How to think about this question

The bridge table approach for the many-to-many relationship between Earnings (quarterly) and DateTable (daily) uses a cross-join of distinct quarters and dates, enabling DAX functions like CALCULATE with USERELATIONSHIP to filter correctly. Under the hood, VertiPaq compresses the imported data column-wise, so a star schema with separate dimension tables (Company, DateTable) and fact tables (StockPrices, Earnings) minimizes cardinality and maximizes compression, which is critical for 1.3M rows. In real-world scenarios, this design allows users to slice by sector and date range while computing moving averages via DAX measures like CALCULATE(AVERAGE(StockPrices[close_price]), DATESINPERIOD(DateTable[Date], MAX(DateTable[Date]), -30, DAY)).

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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this PL-300 question test?

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: Import all tables, create a date table with CALENDAR, and establish relationships: StockPrices[Date] -> DateTable[Date], StockPrices[Ticker] -> Company[Ticker], Earnings[Ticker] -> Company[Ticker], and create a many-to-many relationship between Earnings and DateTable using a bridge table. — Option C is correct because importing all tables into the in-memory VertiPaq engine ensures optimal performance for large datasets (1.3M rows) and complex calculations like moving averages. Creating a separate date table with CALENDAR enables proper time intelligence, while the bridge table resolves the many-to-many relationship between quarterly earnings and daily dates, allowing accurate filtering by sector, company, and date range without performance degradation.

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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