Question 159 of 966

Model the data →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to configure an incremental refresh policy. This is the right choice because incremental refresh in Power BI automatically manages partition creation and refresh for the Sales table based on a date/time column, which directly addresses the need to improve incremental refresh performance. Instead of manually maintaining yearly partitions, the policy refreshes only the most recent data—such as the last five years—while leaving historical partitions untouched, drastically reducing refresh time and resource consumption. On the Microsoft Power BI Data Analyst PL-300 exam, this question tests your understanding of how to optimize data refresh for large Import models, often appearing as a scenario where manual partitioning is already in place but performance is poor. A common trap is to assume that adding more manual partitions or filtering queries will solve the issue, but the core concept is that incremental refresh automates partition management for efficiency. Remember the memory tip: “Manual partitions are static; incremental refresh is dynamic—let the policy do the slicing.”

PL-300 Model the data Practice Question

This PL-300 practice question tests your understanding of model the data. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
```json
{
  "tables": [
    {
      "name": "Sales",
      "columns": [
        {"name": "OrderID", "dataType": "int"},
        {"name": "OrderDate", "dataType": "datetime"},
        {"name": "Amount", "dataType": "decimal"}
      ],
      "partitions": [
        {
          "name": "Partition1",
          "source": {
            "type": "m",
            "expression": "let Source = Sql.Database(\"server\", \"db\"), Sales = Source{[Schema=\"dbo\",Item=\"Sales\"]}[Data], FilteredRows = Table.SelectRows(Sales, each [OrderDate] >= #datetime(2020,1,1) and [OrderDate] < #datetime(2021,1,1)) in FilteredRows"
          }
        },
        {
          "name": "Partition2",
          "source": {
            "type": "m",
            "expression": "let Source = Sql.Database(\"server\", \"db\"), Sales = Source{[Schema=\"dbo\",Item=\"Sales\"]}[Data], FilteredRows = Table.SelectRows(Sales, each [OrderDate] >= #datetime(2021,1,1) and [OrderDate] < #datetime(2022,1,1)) in FilteredRows"
          }
        }
      ]
    }
  ]
}
```

You are reviewing the partition configuration for a Power BI Import model as shown in the exhibit. The table Sales is partitioned by year. You need to modify the model to improve incremental refresh performance. What change should you make?

Question 1mediummultiple choice

Full question →

Exhibit

Refer to the exhibit.
```json
{
  "tables": [
    {
      "name": "Sales",
      "columns": [
        {"name": "OrderID", "dataType": "int"},
        {"name": "OrderDate", "dataType": "datetime"},
        {"name": "Amount", "dataType": "decimal"}
      ],
      "partitions": [
        {
          "name": "Partition1",
          "source": {
            "type": "m",
            "expression": "let Source = Sql.Database(\"server\", \"db\"), Sales = Source{[Schema=\"dbo\",Item=\"Sales\"]}[Data], FilteredRows = Table.SelectRows(Sales, each [OrderDate] >= #datetime(2020,1,1) and [OrderDate] < #datetime(2021,1,1)) in FilteredRows"
          }
        },
        {
          "name": "Partition2",
          "source": {
            "type": "m",
            "expression": "let Source = Sql.Database(\"server\", \"db\"), Sales = Source{[Schema=\"dbo\",Item=\"Sales\"]}[Data], FilteredRows = Table.SelectRows(Sales, each [OrderDate] >= #datetime(2021,1,1) and [OrderDate] < #datetime(2022,1,1)) in FilteredRows"
          }
        }
      ]
    }
  ]
}
```

A
Increase the number of partitions to monthly
Why wrong: More partitions can increase complexity and may not improve performance; incremental refresh is a better approach.
B
Configure incremental refresh policy
Incremental refresh automatically creates and manages partitions based on a date range, optimizing refresh performance.
C
Remove all partitions and load data as a single table
Why wrong: Removing partitions would increase refresh time and data load.
D
Change the storage mode to DirectQuery
Why wrong: DirectQuery avoids partitions but changes query performance characteristics, not directly improving incremental refresh.

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

✓

Configure incremental refresh policy

Configuring an incremental refresh policy (Option B) is the correct approach because it automatically manages partition creation and refresh for the Sales table based on a date/time column. This improves performance by refreshing only the most recent data (e.g., last 5 years) while keeping historical partitions unchanged, reducing refresh time and resource consumption compared to manual yearly partitions.

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.

✗
Increase the number of partitions to monthly
Why it's wrong here
More partitions can increase complexity and may not improve performance; incremental refresh is a better approach.
✓
Configure incremental refresh policy
Why this is correct
Incremental refresh automatically creates and manages partitions based on a date range, optimizing refresh performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove all partitions and load data as a single table
Why it's wrong here
Removing partitions would increase refresh time and data load.
✗
Change the storage mode to DirectQuery
Why it's wrong here
DirectQuery avoids partitions but changes query performance characteristics, not directly improving incremental refresh.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may think increasing partition count (Option A) always improves performance, but in Power BI, too many partitions increase metadata overhead and refresh orchestration time, making incremental refresh policies the correct solution for efficient, automated partition management.

Detailed technical explanation

How to think about this question

In Power BI Import models, incremental refresh policies use RangeStart and RangeEnd parameters to filter source queries, creating a rolling window of partitions. The underlying engine (VertiPaq) stores each partition as a separate segment, so only the modified partition(s) need to be refreshed, reducing data load and memory pressure. A real-world scenario is a large fact table with billions of rows where yearly partitions still require full refreshes of each year; incremental refresh with a daily or monthly granularity ensures only the latest period is processed.

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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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?

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: Configure incremental refresh policy — Configuring an incremental refresh policy (Option B) is the correct approach because it automatically manages partition creation and refresh for the Sales table based on a date/time column. This improves performance by refreshing only the most recent data (e.g., last 5 years) while keeping historical partitions unchanged, reducing refresh time and resource consumption compared to manual yearly partitions.

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.

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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