Question 509 of 760
DP-203 Develop data processing Practice Question
In Azure Synapse Analytics serverless SQL pool, you query Parquet files stored in Azure Data Lake Storage Gen2. You notice that queries are slow. Which configuration change is most likely to improve performance?
⚠ Common exam trap
Many candidates confuse serverless SQL pool with dedicated SQL pool and assume that increasing DWU (a dedicated pool concept) will improve performance, or they think data partitioning alone is sufficient without addressing the schema inference overhead.
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
✓
Use OPENROWSET with a properly defined schema and file format
OPENROWSET with an explicitly defined schema and file format (e.g., FORMAT='PARQUET') enables the serverless SQL pool to bypass schema inference, which is a costly runtime operation. By providing a proper schema and file format, the query engine can directly read the Parquet metadata and column statistics, significantly reducing I/O and CPU overhead. This is the most direct and effective performance tuning change for querying Parquet files in a serverless SQL pool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Partition the data in Azure Data Lake Storage Gen2
Why it's wrong here
Partitioning helps with file pruning but serverless SQL can already benefit from partition elimination.
- ✗
Create a pipeline in Azure Synapse to preprocess the data
Why it's wrong here
Pipelines do not directly improve query performance on serverless SQL.
- ✓
Use OPENROWSET with a properly defined schema and file format
Why this is correct
Specifying schema and file format helps the query optimizer generate efficient execution plans.
- ✗
Increase the DWU setting of the serverless SQL pool
Why it's wrong here
Serverless SQL pool does not have DWU; it auto-scales.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jun 24, 2026
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