DP-203 Design and implement data storage Practice Question
A company uses Azure Synapse Analytics serverless SQL pool to query data in ADLS Gen2. Users report that queries against Parquet files are slow. What should you recommend to improve query performance?
⚠ Common exam trap
Test-takers frequently confuse external table capabilities with dedicated SQL pool features, assuming that indexes like columnstore can be applied to external tables, or that file format changes (CSV) or file count adjustments are the primary performance levers, when in fact statistics are the critical missing piece for serverless SQL pool optimization.
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 external tables with statistics on relevant columns.
In Azure Synapse serverless SQL pool, external tables do not automatically have statistics. Without statistics, the query optimizer cannot generate efficient execution plans, leading to poor performance on Parquet files. Creating statistics on relevant columns enables the optimizer to estimate cardinality and choose better join and filter strategies, significantly improving query speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create external tables with statistics on relevant columns.
Why this is correct
Statistics help the optimizer prune data and improve query performance.
- ✗
Create clustered columnstore indexes on the external tables.
Why it's wrong here
Serverless SQL pool does not support indexes on external tables.
- ✗
Convert the Parquet files to CSV format for faster reads.
Why it's wrong here
CSV is larger and slower to read than Parquet.
- ✗
Partition the data into many small files.
Why it's wrong here
Too many small files increase metadata overhead and slow queries.
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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