DP-203 Develop data processing Practice Question
You have a Data Factory pipeline that runs a U-SQL script in Azure Data Lake Analytics. The script processes terabytes of data and outputs to a CSV file. The pipeline is failing with the error: 'The job failed with UserError: Script execution failed.' You need to troubleshoot the issue. Which approach should you take first?
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
✓
Review the job logs in Azure Data Lake Analytics to identify the specific script error.
The most effective first step is to examine the detailed job logs in Data Lake Analytics, which contain the actual script error. Increasing parallelism or changing output format may not address the underlying script error. Moving to Azure Synapse is a larger architectural change.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the output format to Parquet to reduce file size.
Why it's wrong here
The failure occurs during script execution, before output writing, so changing the sink format cannot resolve a UserError. It is tempting because Parquet reduces file size and speeds up downstream reads, which is the right optimisation when output volume or query performance is the actual problem.
- ✓
Review the job logs in Azure Data Lake Analytics to identify the specific script error.
Why this is correct
Azure Data Lake Analytics records detailed job logs, including the vertex and script error behind UserError failures. Reviewing them first pinpoints the faulty U-SQL statement, avoiding guesswork before changing the terabytes-scale script or pipeline configuration.
- ✗
Migrate the U-SQL script to Azure Synapse Spark pool.
Why it's wrong here
Migrating engines is a redesign, not troubleshooting; it discards the diagnostic detail in the U-SQL job output and cannot be the first step. It is tempting because Spark pools are the strategic replacement for the retiring Data Lake Analytics, so migration is correct as a long-term platform decision.
- ✗
Increase the degree of parallelism for the U-SQL job.
Why it's wrong here
Parallelism cannot fix a UserError, which signals a script-level fault such as a syntax error, missing file or bad schema; raising it just re-runs the same broken script. It is tempting because parallelism tuning genuinely helps VertexFailed or performance-related failures on large jobs.
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