Question 764 of 966

Prepare the data →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to modify the M expression to use a native SQL query with a WHERE clause on OrderDate. This is correct because it enables query folding, which pushes the data filtering operation directly to the source database, dramatically reducing the amount of data transferred and processed by Power BI. By leveraging the database engine’s indexing and query optimization, you achieve a significant reduction in Power BI refresh time while ensuring only the most recent 4 years of data is loaded. On the PL-300 exam, this scenario tests your understanding of query folding versus incremental refresh—a common trap is assuming incremental refresh is always superior, but here a native SQL WHERE clause is more effective because it fully offloads the filtering to the source. Remember the memory tip: “Fold it at the source, not the course”—if you can push filtering to the database, you avoid pulling unnecessary data into Power BI.

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.

Exhibit

Refer to the exhibit.
{
  "dataSources": [
    {
      "name": "SalesDB",
      "connectionString": "Data Source=salesdb.contoso.com;Initial Catalog=Sales;Integrated Security=SSPI;"
    }
  ],
  "dataset": {
    "tables": [
      {
        "name": "Sales",
        "source": {
          "type": "m",
          "expression": "let
  Source = Sql.Database(\"salesdb.contoso.com\", \"Sales\"),
  dbo_Sales = Source{[Schema=\"dbo\",Item=\"Sales\"]}[Data],
  #\"Filtered Rows\" = Table.SelectRows(dbo_Sales, each [OrderDate] >= #date(2020,1,1) and [OrderDate] <= #date(2024,12,31))
in
  #\"Filtered Rows\"
"
        },
        "columns": [
          {"name": "OrderID", "dataType": "Int64"},
          {"name": "OrderDate", "dataType": "DateTime"},
          {"name": "Amount", "dataType": "Decimal"}
        ]
      }
    ]
  }
}

You are reviewing a Power BI dataset definition (in JSON format). The dataset refreshes daily but takes over 2 hours. You need to reduce the refresh time while ensuring that the most recent 4 years of data is always available. What is the most effective change to the dataset?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "always"
Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.

Question 1hardmultiple choice

Full question →

Exhibit

Refer to the exhibit.
{
  "dataSources": [
    {
      "name": "SalesDB",
      "connectionString": "Data Source=salesdb.contoso.com;Initial Catalog=Sales;Integrated Security=SSPI;"
    }
  ],
  "dataset": {
    "tables": [
      {
        "name": "Sales",
        "source": {
          "type": "m",
          "expression": "let
  Source = Sql.Database(\"salesdb.contoso.com\", \"Sales\"),
  dbo_Sales = Source{[Schema=\"dbo\",Item=\"Sales\"]}[Data],
  #\"Filtered Rows\" = Table.SelectRows(dbo_Sales, each [OrderDate] >= #date(2020,1,1) and [OrderDate] <= #date(2024,12,31))
in
  #\"Filtered Rows\"
"
        },
        "columns": [
          {"name": "OrderID", "dataType": "Int64"},
          {"name": "OrderDate", "dataType": "DateTime"},
          {"name": "Amount", "dataType": "Decimal"}
        ]
      }
    ]
  }
}

A
Add an index on the OrderDate column in the source database.
Why wrong: This is a database-side optimization, not a dataset change.
B
Remove the Amount column from the dataset.
Why wrong: Removing columns does not reduce row count; the main issue is row volume.
C
Implement Incremental Refresh policy with a date range filter on OrderDate.
Why wrong: Incremental refresh works with partitions; the current query does not have the required parameters.
D
Modify the M expression to use a native SQL query with a WHERE clause on OrderDate.
This enables Query Folding, reducing data transferred.

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

✓

Modify the M expression to use a native SQL query with a WHERE clause on OrderDate.

Option D is correct because using a native SQL query with a WHERE clause on OrderDate pushes the filtering of data to the source database, reducing the amount of data transferred and processed by Power BI. This is the most effective change to reduce refresh time while ensuring the most recent 4 years of data is available, as it leverages the database engine's indexing and query optimization capabilities.

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.

✗
Add an index on the OrderDate column in the source database.
Why it's wrong here
This is a database-side optimization, not a dataset change.
✗
Remove the Amount column from the dataset.
Why it's wrong here
Removing columns does not reduce row count; the main issue is row volume.
✗
Implement Incremental Refresh policy with a date range filter on OrderDate.
Why it's wrong here
Incremental refresh works with partitions; the current query does not have the required parameters.
✓
Modify the M expression to use a native SQL query with a WHERE clause on OrderDate.
Why this is correct
This enables Query Folding, reducing data transferred.
Clue confirmation
The clue word "always" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose Incremental Refresh (Option C) thinking it automatically reduces refresh time, but they overlook that the question requires the most effective change and that native SQL query pushdown can be more efficient for reducing data volume at the source, especially when the source database supports query optimization.

Detailed technical explanation

How to think about this question

When Power BI uses a native SQL query with a WHERE clause, the query is executed directly on the source database, allowing the database engine to use indexes and only return the filtered rows. This reduces network transfer and Power BI's data processing load, which is critical for large datasets. In contrast, Power Query's default folding may not always push filters to the source, especially with complex M expressions, leading to full table scans and slower refresh times.

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: Modify the M expression to use a native SQL query with a WHERE clause on OrderDate. — Option D is correct because using a native SQL query with a WHERE clause on OrderDate pushes the filtering of data to the source database, reducing the amount of data transferred and processed by Power BI. This is the most effective change to reduce refresh time while ensuring the most recent 4 years of data is available, as it leverages the database engine's indexing and query optimization capabilities.

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: "always". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 24, 2026

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