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DP-203 Practice Question: You have an Azure Data Factory (ADF) pipeline…
You have an Azure Data Factory (ADF) pipeline that runs hourly to ingest data from an on-premises SQL Server into Azure Data Lake Storage Gen2. The pipeline includes a Copy activity that transfers all rows from a source table 'Sales' (approximately 10 million rows) to a Parquet file in the data lake. Recently, you notice that the pipeline runtime has increased from 15 minutes to over an hour. The source database CPU utilization is normal, and the network bandwidth is not saturated. You check ADF monitoring and see high 'Data integration unit' consumption and frequent 'BlobWrite' throttling errors. The storage account is in the same region as the ADF. You need to reduce the pipeline runtime. What should you do?
⚠ Common exam trap
Candidates often assume throttling errors require a storage tier upgrade (Option A) or a compute change (Option C), when the real solution is to reduce the volume of data written per execution by implementing incremental loading.
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
✓
Modify the pipeline to use incremental loads instead of full loads each time.
The pipeline runtime has increased due to frequent BlobWrite throttling errors, indicating that the storage account is hitting its write request limits. By modifying the pipeline to use incremental loads instead of full loads each hour, you reduce the volume of data written per execution, which lowers the number of write operations and avoids throttling. This directly addresses the root cause without requiring a storage tier upgrade or a complete 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 storage account to Premium tier to increase throughput limits.
Why it's wrong here
Would increase cost but not fix the inefficiency of loading all data every hour.
- ✓
Modify the pipeline to use incremental loads instead of full loads each time.
Why this is correct
Reduces data volume per run, decreasing storage throttling and runtime.
- ✗
Replace the Copy activity with an Azure Databricks notebook to process the data.
Why it's wrong here
Would add complexity and cost; throttling issue likely from data volume, not compute.
- ✗
Use PolyBase in the Copy activity to load data directly into Azure Synapse Analytics.
Why it's wrong here
PolyBase is not for ADF copy performance; it's for loading into Synapse.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.