Question 230 of 760
DP-203 Optimize folder structure Practice Question
You are designing a data lake on Azure Data Lake Storage Gen2. The data will be used by both batch processing (Spark) and interactive querying (Azure Synapse Serverless SQL). The data is partitioned by date and stored as Parquet. What is the optimal folder structure to minimize cross-partition scans for both workloads?
⚠ Common exam trap
A common mix-up: candidates assume a flat date-based folder or filename pattern is sufficient for partitioning, but both Spark and Synapse Serverless SQL require hierarchical folder structures to enable automatic partition pruning and avoid full scans.
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
✓
/year/month/day/ (e.g., /2023/12/25/)
(/year/month/day/) is optimal because it aligns with Hive-style partitioning, which both Spark and Azure Synapse Serverless SQL can leverage for partition pruning. Spark uses partition discovery to read only relevant directories, and Synapse Serverless SQL uses the file path metadata to filter partitions, minimizing cross-partition scans and reducing data read overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
All files in a single folder
Why it's wrong here
No partitioning at all, causing full scans.
- ✗
/yyyy-mm-dd/ (e.g., /2023-12-25/)
Why it's wrong here
Single-level partitioning does not allow efficient pruning for yearly or monthly queries.
- ✗
Files named by date (e.g., data_20231225.parquet)
Why it's wrong here
Partition pruning requires folder hierarchy, not file names.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-203 exam frequently reuses these exact scenarios with slightly different constraints.
✓/year/month/day/ (e.g., /2023/12/25/)Correct answer▾
✗All files in a single folderWrong answer — click to see why▾
Why this is wrong here
No partitioning at all, causing full scans.
✗/yyyy-mm-dd/ (e.g., /2023-12-25/)Wrong answer — click to see why▾
Why this is wrong here
Single-level partitioning does not allow efficient pruning for yearly or monthly queries.
✗Files named by date (e.g., data_20231225.parquet)Wrong answer — click to see why▾
Why this is wrong here
Partition pruning requires folder hierarchy, not file names.
Analysis generated from the official DP-203blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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 11, 2026
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