PL-300 Prepare the data Practice Question
You need to prepare data from a folder containing multiple CSV files with identical structure. What is the most efficient way to load all files into a single table?
⚠ Common exam trap
Watch out — candidates often think manual appending (Option A) is simpler or that Python scripting (Option C) is a valid Power Query feature, but the exam tests knowledge of Power Query's native 'Combine Files' functionality as the most efficient and integrated method.
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
✓
Use the 'From Folder' data source and then click 'Combine & Transform Data'
The 'From Folder' data source in Power Query automatically detects multiple CSV files with identical structure and provides a 'Combine & Transform Data' button that merges them into a single table in one step. This is the most efficient method as it eliminates the need for manual imports or scripting, leveraging Power Query's built-in file combination logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Import each CSV file separately and then append them
Why it's wrong here
Manually importing each CSV file and then using an Append query is inefficient and brittle. This approach requires you to repeat the process for every new file, manually maintain the same column order and data types across imports, and any schema mismatch can cause errors during append. It also bypasses Power Query's folder-level transformation logic, making the solution non-scalable and hard to refresh.
- ✓
Use the 'From Folder' data source and then click 'Combine & Transform Data'
Why this is correct
The 'From Folder' data source with 'Combine & Transform Data' is the native, dynamic solution. It prompts Power Query to generate a sample-file query, a transformation function, and a final combined output that automatically applies the same steps to every CSV in the folder. On refresh, newly added or removed files are reflected without manual intervention, and schema changes are detected and promoted automatically.
- ✗
Write a Python script in Power Query to read and combine files
Why it's wrong here
Power Query does not natively support Python for reading files in a folder; the M language is the only query language used inside Power Query steps. While Power BI supports Python scripts as a data source or for transformations on an existing table, it cannot be used to orchestrate the combination of multiple folder files directly. This approach would require a custom Python data connector or external scripting, which is outside the Power Query runtime.
- ✗
Use a dataflow to connect to the folder and apply the 'Combine Files' transformation
Why it's wrong here
A dataflow can connect to a folder and apply the 'Combine Files' transformation, but it is not required for this task and adds unnecessary complexity for a Desktop-level data preparation job. Dataflows are designed for shared, reusable data preparation in the Power BI service, whereas Power Query Desktop already provides the same folder-combination capability locally. Choosing a dataflow would also introduce an extra hop to the cloud and additional latency for what can be done directly in the report.
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