DP-203 Develop data processing Practice Question
You are optimizing an Azure Synapse serverless SQL pool query that queries Parquet files in Azure Data Lake Storage. The query takes longer than expected. You notice that the query reads more data than necessary. What is the most effective way to reduce the amount of data scanned?
⚠ Common exam trap
Test-takers frequently confuse column pruning (reducing columns) with partition pruning (reducing rows), or assume that file size optimization alone reduces data volume, when in fact partition elimination is the key technique for minimizing scanned data 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
✓
Create external tables with explicit schema and partition by a frequently filtered column
Partitioning external tables in Azure Synapse serverless SQL allows the query engine to perform partition elimination, reading only the subdirectories that match the filter criteria. This directly reduces the amount of data scanned from Parquet files in ADLS, addressing the core issue of reading unnecessary data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Split large Parquet files into smaller files of 100 MB each
Why it's wrong here
Splitting files does not reduce the amount of data scanned unless combined with partition pruning; smaller files can increase overhead. This is not the most effective method.
- ✓
Create external tables with explicit schema and partition by a frequently filtered column
Why this is correct
Creating external tables with explicit schema and partitioning allows the serverless SQL pool to perform partition elimination, reading only the relevant directories matching filter conditions, thereby significantly reducing data scanned.
- ✗
Use SELECT with column pruning to only retrieve necessary columns
Why it's wrong here
Column pruning does reduce data scanned because Parquet files are columnar, so only the selected columns are read. However, partition pruning (option B) is more effective as it eliminates entire rows based on filter criteria, reducing data scanned even further when filters involve partitioned columns.
- ✗
Increase the query's resource allocation by using a larger service level objective
Why it's wrong here
Increasing resource allocation (SLO) improves query performance but does not reduce the amount of data scanned; it only speeds up processing of the same 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 |
Go deeper
Related to this question
About these practice questions
Courseiva writes every DP-203 question from scratch — 760 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.