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%) 
   --> Profile and clean the data 
      --> Resolve data import errors

Note that there are 10 practice questions (with answers and explanations) at the end of 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's main page.

Data import errors are a common issue when bringing data into Power BI. These errors typically arise during the Power Query stage and must be resolved before data can be successfully loaded into the data model. The PL-300 exam tests your ability to identify, interpret, and fix these errors using Power Query’s built-in tools and transformations.

What Are Data Import Errors?

Import errors occur when Power BI cannot process or convert incoming data as expected. These errors can arise from:

• Invalid data formats
• Incompatible data types
• Data corruption
• Unexpected null or missing values
• Transformation steps that fail

Identifying and resolving these errors early ensures that your dataset is clean, consistent, and ready for modeling and reporting.

Where Import Errors Occur

Import errors are most commonly encountered:

🧩 During Data Type Conversion

When the source value cannot be converted to the target type
(e.g., text "N/A" converted to number)

🧩 In Applied Steps

If a transformation step references a column that doesn’t exist
or expects a format that isn’t present

🧩 While Combining Queries

When merging or appending tables with mismatched structures

🧩 When Parsing Complex Formats

Such as dates in nonstandard formats or malformed JSON

How Power BI Signals Import Errors

In Power Query Editor, import errors are typically shown as:

• Error icons in the preview cells
• A warning message in the query results (“Error” link)
• Red dotted underlines or warnings in applied steps
• The “Load failed” message when refreshing

The first step in resolving errors is to examine the error details.

Viewing Error Details

When an error appears in Power Query:

1. Click the Error indicator in the cell or
2. Use View → Column quality / Column profile

You can also filter the column to show only error values by filtering on Errors.

Exam tip:
Power BI often shows technical error messages, so part of the task is interpreting what the underlying issue is (e.g., type mismatch, invalid format, null where not expected).

Common Import Errors & How to Fix Them

1. Type Conversion Errors

Scenario: A column expected to be numeric contains text such as "Unknown".

Fix Options:

• Use Replace Errors to substitute a default value
• Use Replace Values to convert specific text to numeric (e.g., "Unknown" → 0)
• Adjust data type after cleaning

Key Idea: Always fix the root cause before changing the data type.

2. Unexpected Null Values

Scenario: A key column has nulls where values are required, causing subsequent transformations to fail.

Fix Options:

• Replace nulls with default values via Replace Values
• Remove rows where the column is null
• Use conditional logic (Add Column → Conditional Column) to handle nulls appropriately

Key Idea: Nulls can break transformations (like merges) if not handled first.

3. Transformation Step Errors

Scenario: A transformation step refers to a column removed or renamed earlier in the applied steps.

Fix Options:

• Review and reorder steps in the APPLIED STEPS pane
• Rename the column consistently before referencing it
• Delete the problematic step and reapply it correctly

Key Idea: Power BI applies steps sequentially. A downstream step can fail if an upstream change invalidates assumptions.

4. Merge/Append Structure Errors

Scenario: You merge or append tables that don’t share compatible column structures (e.g., mismatched data types).

Fix Options:

• Ensure columns used for merger/join have identical data types
• Rename or reorder columns to match structures
• Preclean individual tables before combining

Key Idea: Always validate structure and types before merging or appending tables.

5. Parsing & Date Format Errors

Scenario: Date values import as text due to regional format differences (MM/DD/YYYY vs DD/MM/YYYY).

Fix Options:

• Change the column data type to Date after validating format
• Use Transform → Using Locale to define the correct regional format
• Use Custom Columns to parse dates manually with Date.FromText

Key Idea: Locale-aware parsing helps resolve ambiguous date formats.

Tools to Help Diagnose Import Errors

Power BI provides several tools to help you locate and fix import errors:

🔍 Error Filtering

Filter columns to show only error rows.

📊 Column Quality / Distribution / Profile

Use profiling tools to identify patterns, nulls, and anomalies.

🧠 Step Validation

Hover over each Applied Step to see whether it is valid or failing.

📝 Advanced Editor

Review M code for logic errors or incorrect references.

Best Practices for Fixing Import Errors

1. Clean Before Converting Types
Always fix textual anomalies and nulls before assigning data types.

2. Avoid Hard-Coding Values
Replace problematic values using conditional logic or parameters for maintenance.

3. Inspect Impact of Each Step
Use the Applied Steps pane to ensure each transformation is valid.

4. Test Incrementally
Fix errors one at a time and refresh often to confirm success.

5. Document Assumptions
Add comments or descriptive step names to make logic clearer.

How This Appears on the PL-300 Exam

The exam commonly tests your ability to:

✔ Identify why a query fails (type mismatch, nulls, missing column)
✔ Choose the correct sequence to fix the issue
✔ Understand the difference between Replace Errors and Remove Errors
✔ Apply transformations in the correct order (clean → convert → transform)

Most questions are scenario-based, asking what action you would take next to successfully import data.

Key Exam Takeaways

• Import errors can be caused by data type mismatches, unexpected nulls, invalid formats, and broken transformation steps.
• Use Power Query tools to diagnose and resolve errors before loading data into the model.
• Always understand the root cause before applying a fix.
• Knowing how to use Replace Errors, Replace Values, Conditional Columns, and Data Type changes is essential.

Practice Questions

Go to the Practice Exam Questions for this topic.