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 limits how many queries run simultaneously, not how much data each scans; raising it leaves the date-filter scan cost unchanged and can increase resource contention. It tempts because throughput tuning sounds like performance tuning, and would help when many users queue behind the limit.
- ✗
Provision a dedicated SQL pool with more DTUs
Why it's wrong here
Provisioning a dedicated SQL pool introduces ongoing compute charges, directly violating the no-added-cost requirement, and requires loading data rather than querying the Parquet files in place. It tempts because dedicated pools deliver faster queries, and would be correct when sustained, predictable workloads justify fixed capacity.
- ✗
Create a clustered columnstore index on the date column
Why it's wrong here
Serverless SQL pool queries external files and cannot create clustered columnstore indexes on them; that index applies to tables in a dedicated SQL pool. It tempts because columnstore indexing genuinely accelerates date filtering, but only where data is materialised in a table.
- ✓
Partition the data by date in the data lake (e.g., folder structure: /year=*/month=*/day=*)
Why this is correct
Partitioning Parquet files into year/month/day folders lets the serverless SQL pool prune irrelevant files, reading only matching partitions instead of scanning the whole dataset. This reduces data scanned per query, improving performance without raising cost, since serverless billing is per terabyte processed.
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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