DP-203 Design and implement data storage Practice Question
You are a data engineer at a healthcare analytics company. The company stores patient records in an Azure Data Lake Storage Gen2 account organized by /patient/{patientId}/year={yyyy}/month={MM}/day={dd}/*.parquet. There are 10,000 patients, and each patient has about 1 GB of data per year. The data is used by data scientists who run ad-hoc queries using Azure Synapse Serverless SQL. They complain that queries scanning multiple patients over the last year take too long and consume too much data. They often need to filter by patientId and a date range. You need to improve query performance and reduce the amount of data scanned. You cannot change the folder structure because it is used by other processes. What should you do?
⚠ Common exam trap
It's easy for candidates to think views can improve performance, but in Synapse Serverless SQL, views are non-materialized and do not reduce data scanned unless the underlying data is partitioned and queries filter on partition columns.
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 in Synapse Serverless SQL that use the folder structure as partitions, and ensure queries filter on year, month, and day.
Creating external tables in Synapse Serverless SQL with the existing folder structure as partitions allows the query engine to perform partition elimination. When queries filter on year, month, and day, Synapse Serverless SQL will only scan the relevant folders, drastically reducing data scanned and improving performance. This approach does not require changing the folder structure, which is used by other processes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reorganize the folder structure to /year={yyyy}/month={MM}/day={dd}/patientId={patientId}/*.parquet.
Why it's wrong here
Changing folder structure is not allowed as per the requirement.
- ✗
Convert the Parquet files to CSV format to improve compression and reduce file size.
Why it's wrong here
CSV is less efficient than Parquet for analytical queries.
- ✗
Create views that aggregate data by patient and date, and instruct data scientists to query the views.
Why it's wrong here
Views do not automatically prune partitions unless filters are applied on partition columns.
- ✓
Create external tables in Synapse Serverless SQL that use the folder structure as partitions, and ensure queries filter on year, month, and day.
Why this is correct
External tables with partition elimination reduce scanned data.
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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