DP-203 Develop data processing Practice Question
You are optimizing a pipeline in Azure Data Factory that copies data from Azure Blob Storage to Azure Synapse Analytics. The pipeline uses a copy activity with PolyBase. The data is partitioned by date in Blob Storage. You notice that the load is slow. What is the most likely cause?
⚠ Common exam trap
The trap here is that candidates often focus on file format (Parquet vs. CSV) or storage type (Blob vs. ADLS Gen2) as the primary performance factor, when in reality the number and size of files is a more common and impactful bottleneck in PolyBase loads.
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
✓
The source files are too many and too small (e.g., thousands of 1 MB files)
PolyBase in Azure Synapse Analytics performs best when reading large, contiguous files. When the source contains thousands of small files (e.g., 1 MB each), PolyBase must initiate a separate read operation for each file, causing excessive overhead from file open/close operations and metadata requests. This dramatically reduces throughput compared to reading fewer, larger files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The source files are stored in Azure Blob Storage instead of Data Lake Storage Gen2
Why it's wrong here
PolyBase works with Blob Storage.
- ✗
The source files are in CSV format instead of Parquet
Why it's wrong here
PolyBase supports CSV, so this is not a significant issue.
- ✓
The source files are too many and too small (e.g., thousands of 1 MB files)
Why this is correct
Many small files cause overhead; PolyBase is optimized for fewer, larger files.
- ✗
The sink table has a clustered columnstore index
Why it's wrong here
Columnstore is actually beneficial for PolyBase loading.
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.