DP-203 Develop data processing Practice Question
Your organization uses Azure Synapse Analytics serverless SQL pool to query Parquet files in Azure Data Lake Storage Gen2. You notice that queries are slow when filtering on a date column. You need to improve query performance without increasing costs. What should you do?
⚠ Common exam trap
Many exam-takers confuse serverless SQL pool with dedicated SQL pool and incorrectly choose to create indexes or scale resources, not realizing that serverless SQL pool relies on external data partitioning and file-skipping techniques rather than internal indexing or provisioning.
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
✓
Partition the data by date in the data lake (e.g., folder structure: /year=*/month=*/day=*)
Partitioning the data by date in the data lake (e.g., /year=*/month=*/day=*) allows the serverless SQL pool to leverage partition elimination. When querying with a filter on the date column, the pool can read only the relevant partitions (folders) instead of scanning all Parquet files, drastically reducing I/O and improving query performance at no additional cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum query concurrency limit
Why it's wrong here
Concurrency does not affect query speed.
- ✗
Provision a dedicated SQL pool with more DTUs
Why it's wrong here
Serverless SQL pool is serverless; DTUs are not used.
- ✗
Create a clustered columnstore index on the date column
Why it's wrong here
Serverless SQL pool does not support creating indexes on external data.
- ✓
Partition the data by date in the data lake (e.g., folder structure: /year=*/month=*/day=*)
Why this is correct
Partition elimination reduces 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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