Question 411 of 760
DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Synapse Analytics that uses serverless SQL pools to query Parquet files in Azure Data Lake Storage Gen2. The files are partitioned by year and month. You need to optimize query performance and reduce data scanned. What should you do?
⚠ Common exam trap
Many candidates assume that simply using external tables or OPENROWSET automatically provides partition pruning, but in serverless SQL pools, partition elimination only occurs when the query explicitly references the partition columns in the WHERE clause, typically through a view or inline filter.
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
✓
Create views that filter on partition columns.
Serverless SQL pools in Azure Synapse Analytics support partition elimination only when queries use views or inline queries that explicitly filter on partition columns (e.g., year, month) in the WHERE clause. This allows the pool to skip scanning irrelevant partitions, reducing data scanned and improving performance. Creating views that encapsulate these filters ensures consistent partition pruning across queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use CREATE EXTERNAL TABLE AS SELECT (CETAS) to create new external tables.
Why it's wrong here
CETAS is for output, not query optimization.
- ✗
Use OPENROWSET with the DATA_SOURCE parameter.
Why it's wrong here
Does not enable partition elimination.
- ✓
Create views that filter on partition columns.
Why this is correct
Allows partition elimination when querying.
- ✗
Increase the number of files per partition.
Why it's wrong here
Does not reduce data scanned.
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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