DP-203 Develop data processing Practice Question
You are using Azure Data Lake Storage Gen2 as the data lake for your organization. You need to process files in the 'incoming' folder using a scheduled Azure Databricks notebook. After processing, the files should be moved to the 'processed' folder. The files are large (up to 10 GB) and you want to minimize the time to move them. Which approach should you use?
⚠ Common exam trap
Watch out — candidates often assume moving large files requires copying, but Azure Data Lake Storage Gen2's hierarchical namespace enables instant metadata-only renames, making `dbutils.fs.mv()` the optimal choice.
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 Azure Databricks dbutils.fs.mv() to move the file.
`dbutils.fs.mv()` performs a metadata-only rename operation on Azure Data Lake Storage Gen2, which is instantaneous regardless of file size. This avoids any data movement, making it the fastest approach for moving large files (up to 10 GB) between folders within the same storage account.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the Azure Databricks dbutils.fs.mv() to move the file.
Why this is correct
The move operation in ADLS Gen2 is a metadata rename, which is instantaneous.
- ✗
Use Azure Data Factory with a Copy activity to move the file, then delete the source.
Why it's wrong here
ADF copy also performs a data copy, which is slow for large files.
- ✗
Change the file's metadata to update its directory path.
Why it's wrong here
File metadata does not include directory path; the path is part of the file system hierarchy.
- ✗
Use the Azure Databricks dbutils.fs.cp() to copy the file to the processed folder, then delete the original.
Why it's wrong here
Copying is a data operation that takes time for large files.
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