DP-203 Develop data processing Practice Question
A data engineer is building a pipeline in Azure Data Factory to load data from Azure Blob Storage to Azure Synapse Analytics using PolyBase. The pipeline must handle large files efficiently and minimize load time. Which TWO actions should the engineer take? (Choose two.)
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
✓
Split large files into multiple smaller files (e.g., 256 MB each).
To optimize PolyBase loads for large files, splitting files into smaller chunks (e.g., 256 MB) enables parallel processing and reduces load time. Configuring PolyBase with the 'RejectValue' option allows handling of bad rows without failing the entire load, improving resilience. Option B (using a staging table) is not a PolyBase-specific optimization; staging is used for other loading methods like COPY INTO. Option C (converting to Parquet) is not directly related to PolyBase performance and is not required. Option D (copy activity with staging) may use PolyBase but the question asks for PolyBase-specific actions, and staging is not a PolyBase feature. Therefore, the correct actions are A and E.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Split large files into multiple smaller files (e.g., 256 MB each).
Why this is correct
Enables parallelism.
- ✗
Use a staging table to stage the data before loading.
Why it's wrong here
PolyBase loads directly to the target table.
- ✗
Convert files to Parquet format for better compression.
Why it's wrong here
PolyBase supports only text files and ORC/Parquet via external tables, but not directly.
- ✗
Use the copy activity with staging enabled.
Why it's wrong here
That is a different approach, not specific to PolyBase optimization.
- ✓
Configure PolyBase settings to use 'RejectValue' for handling bad records.
Why this is correct
Allows pipeline to continue on errors.
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 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.
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.
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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.