DP-203 Design and implement data storage Practice Question
You are using Azure Synapse Analytics serverless SQL pool to query Parquet files in Azure Data Lake Storage Gen2. The query is slow and you suspect that the file layout is not optimized. You examine the files and find that each file is 50 MB. What should you do to improve query performance?
⚠ Common exam trap
The trap here is that candidates often focus on compression or format conversion to improve performance, but the real bottleneck in serverless SQL pool with many small files is the metadata and scheduling overhead, not the data size itself.
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
✓
Merge the small files into larger files of at least 100 MB each
Azure Synapse serverless SQL pool performs best when reading files of at least 100 MB each. Small files (50 MB) cause excessive metadata operations, partition discovery, and I/O overhead, leading to slower queries. Merging them into larger files reduces the number of file open/close operations and improves parallelism efficiency.
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 into folders by date
Why it's wrong here
Partition pruning helps but does not solve the small file problem.
- ✗
Compress the files with Gzip
Why it's wrong here
Compression reduces size but does not reduce the number of files.
- ✗
Convert the files to CSV format to reduce overhead
Why it's wrong here
CSV is not optimized for analytics and would be slower.
- ✓
Merge the small files into larger files of at least 100 MB each
Why this is correct
Larger files reduce metadata overhead and improve query performance.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.