DP-203 Develop data processing Practice Question
You are troubleshooting a slow-running pipeline in Azure Data Factory. The pipeline copies data from an on-premises SQL Server to Azure Synapse Analytics using a self-hosted integration runtime. The copy activity is using the 'Auto' copy method. You notice that network bandwidth is limited. Which configuration change would most likely improve performance?
⚠ Common exam trap
Many candidates assume increasing DIU or changing the copy method directly speeds up data movement, when in fact the real bottleneck is the network link, and only staging with PolyBase offloads the heavy data transfer to Azure's internal network.
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
✓
Enable staging using Azure Blob Storage and use PolyBase to load into Synapse
When network bandwidth is limited, staging data in Azure Blob Storage allows the copy activity to use PolyBase, which leverages Azure's internal high-speed network for the final load into Synapse. This bypasses the constrained on-premises-to-cloud link for the bulk of the data transfer, significantly improving throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable staging using Azure Blob Storage and use PolyBase to load into Synapse
Why this is correct
Staging improves performance by using parallel uploads to Blob Storage.
- ✗
Increase the Data Integration Units (DIU) for the copy activity
Why it's wrong here
DIU applies to Azure IR, not self-hosted IR.
- ✗
Change the copy method to 'Bulk insert'
Why it's wrong here
Bulk insert is not available for on-premises sources.
- ✗
Set the Fault Tolerance option to skip incompatible rows
Why it's wrong here
This handles errors, not performance.
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 |
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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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.