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DP-203 Design and implement data storage Practice Question

A healthcare company stores patient records in Azure Data Lake Storage Gen2. The data must be organized for efficient querying by a Synapse Analytics serverless SQL pool. The data is currently stored as many small CSV files in a flat directory. You need to improve query performance and reduce the cost of scanning unnecessary data. What should you do?

⚠ Common exam trap

The trap here is focusing on file count or storage service rather than the data format and partitioning, which are the primary drivers of query performance and cost in serverless SQL pools.

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

✓

Convert the data to Parquet format and partition it by admission date.

Converting to Parquet, a columnar format, enables column pruning and better compression, reducing the amount of data scanned. Partitioning by admission date allows the serverless SQL pool to prune partitions based on query filters. Together, these changes optimize query performance and lower 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.

  • ✗

    Combine all CSV files into one large CSV file.

    Why it's wrong here

    Combining files into a single large CSV file may reduce the number of files but does not provide columnar storage or partition pruning. CSV is row-based, so queries still read all columns even if only a few are needed. This approach does not leverage the performance benefits of columnar formats or partitioning, and large files can be unwieldy.

  • ✗

    Create an external table in the serverless SQL pool that points to the CSV files.

    Why it's wrong here

    Creating an external table over CSV files does not change the underlying format; queries will still scan all columns and files unless filters are applied. While external tables provide a schema, they do not inherently improve performance or reduce cost for CSV data. The data remains row-based and unpartitioned, leading to full scans.

  • ✗

    Move the data to Azure Blob Storage and enable hierarchical namespace.

    Why it's wrong here

    Moving to Blob Storage with hierarchical namespace (ADLS Gen2) is already the current storage. Enabling hierarchical namespace does not change the data format or partitioning. It provides directory semantics but does not improve query performance for CSV files. The key issue is the file format and layout, not the storage service.

  • ✓

    Convert the data to Parquet format and partition it by admission date.

    Why this is correct

    Parquet is a columnar format that enables efficient compression and column pruning, reducing I/O and cost. Partitioning by admission date allows the serverless SQL pool to skip irrelevant partitions, further minimizing data scanned. This combination significantly improves query performance and reduces cost for date-filtered queries.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed September 2026 · checked against the official Microsoft exam blueprint

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.