This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Transform and load the data
      --> Configure Data Loading for Queries

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

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Power BI doesn’t just connect to data — it decides what to load and when to load it. Configuring data loading properly ensures your model contains only the necessary data, improves performance, and aligns with business requirements.

In the context of the PL-300: Microsoft Power BI Data Analyst exam, you’ll be expected to understand how to control which queries load to the data model, use query folding where possible, and manage refresh settings appropriately.

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Why Configuring Data Loading Matters

Before discussing how to configure data loading, it’s important to understand why it matters:

• Model performance — unnecessary tables and columns consume memory and slow visuals
• Refresh efficiency — fewer loaded objects means faster refresh
• Manageability — only relevant data should end up in the model
• Clarity — clean, minimal data models reduce mistakes and confusion

Power BI uses Power Query Editor as the staging area for all transformations and loading decisions.

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Key Concepts

1. Enable Load vs Disable Load

Each query in Power Query has a toggle called “Enable Load” (or “Load to model”).

• Enabled: The resulting table will load into the data model
• Disabled: The query runs for transformations but does not create a table in the model

Common Usage:

• Use Disable Load for staging or helper queries that feed other queries but aren’t needed as standalone tables in the model
• Ensure only final tables are loaded into the model

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2. Staging Queries

A staging query is a query used exclusively to prepare data for other queries. It should usually have Enable Load turned off so it doesn’t clutter the model.

Example:

• A staging query cleans raw data
• Final queries reference it
• Only final queries load to the model

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3. Query Dependencies

In Power BI Desktop, View → Query Dependencies shows a visual map of how queries relate.

• Staging queries feed final tables
• Ensures understandability and data lineage
• Highlights which queries are loaded and which are not

Understanding query dependencies helps validate that:

• Only the intended tables are loaded
• Intermediate queries aren’t unnecessary

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4. Incremental Refresh

Incremental refresh allows Power BI to refresh only new or changed data rather than the entire dataset.

Why this matters:

• Essential for large datasets
• Reduces refresh time and resource usage
• Requires configuration in the Power BI Service and on tables with a date/time column

Incremental refresh is usually enabled in Table Settings with parameters like:

• RangeStart
• RangeEnd

These parameters determine the portion of data to refresh.

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5. Query Folding

Query folding refers to the ability of Power Query to push transformations back to the source (e.g., SQL Server).

Why it matters:

• Performance: operations happen at source
• Large data sets benefit most

Configuration that enables query folding includes:

• Filtering early
• Aggregating early
• Avoiding operations that break folding (e.g., certain custom columns)

While not strictly a “loading” setting, query folding directly affects how Power BI retrieves and loads data.

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How to Configure Data Loading

In Power Query Editor

Disable Loading for Specific Queries

1. Right-click the query
2. Uncheck Enable Load
3. Optional: Uncheck Include in report refresh

This prevents the query from creating a model table.

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In the Data View (or Model View)

After loading:

• Hide unnecessary columns
• Hide unused tables from report view
• Rename tables for clarity

Note: Hiding doesn’t remove the data — it simply declutters the field list.

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Incremental Refresh Setup

To enable incremental refresh:

1. Identify a Date/Time column
2. Define RangeStart and RangeEnd parameters
3. Use these parameters to filter the date column
4. Enable Incremental Refresh in table settings

Power BI then only refreshes the relevant partition of data.

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Best Practices

Load MINIMAL Necessary Tables

Avoid loading:

• Staging queries
• Helper queries
• Intermediate transformations

Disable Load Early

This prevents clutter and improves refresh times.

Use Descriptive Names

Query and table names should reflect final usage (e.g., FactSales, DimProduct).

Understand Dependencies

Always validate that disabling load on a query won’t break dependent queries.

Preserve Query Folding

Design transformations that can be folded to source — especially for large data.

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Common Mistakes (Often Tested)

❌ Loading staging queries into the model

This increases model size unnecessarily.

❌ Forgetting to define a key date column when setting up incremental refresh

Incremental refresh requires a proper date/time column.

❌ Breaking query folding early

Certain transformations can prevent folding and slow down refresh.

❌ Changing load settings after building relationships

Altering load settings on queries used in relationships can cause broken models.

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How This Appears on the PL-300 Exam

The exam may present scenarios like:

• A model has slow refresh times. What could you configure to improve efficiency?
• Which queries should be loaded into the model?
• How do staging queries affect model size?
• When should incremental refresh be used?

Exam questions often expect you to explain the impact of loading decisions on performance and maintainability.

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Quick Decision Guide

Scenario	Recommended Configuration
Helper query only used for transformations	Disable Load
Main dimensional table	Enable Load
Large historical dataset	Use Incremental Refresh
Query with steps that can be pushed to source	Ensure Query Folding

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Final PL-300 Takeaways

• Enable Load controls whether a query creates a model table
• Disable Load for staging/helper queries
• Incremental Refresh accelerates large dataset refresh
• Query Folding improves performance during load
• Validate via View Query Dependencies

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Practice Questions

Go to the Practice Exam Questions for this topic.