DP-203 Develop data processing Practice Question
You are a data engineer at a retail company. You need to develop a data processing solution in Azure Synapse Analytics that reads sales transactions from Parquet files stored in Azure Data Lake Storage Gen2, transforms the data by aggregating daily sales per store, and writes the results to a dedicated SQL pool table for reporting. The transformation logic must be reusable and maintained in a source control system. You want to minimize administrative overhead and leverage serverless resources where possible. Which approach should you recommend?
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
✓
Use a serverless SQL pool to query the Parquet files via OPENROWSET, then use CETAS to write the aggregated results to the dedicated SQL pool using an external table.
A serverless SQL pool can directly query Parquet files using OPENROWSET, and the CREATE EXTERNAL TABLE AS SELECT (CETAS) statement can transform and store aggregated results in a dedicated SQL pool table via PolyBase. This approach uses serverless resources for transformation, minimizing administrative overhead and avoiding the need to manage Spark pools. Option A is incorrect because Azure Data Factory with Mapping Data Flows runs on Spark clusters, which adds administrative overhead and does not align with the goal of minimizing management. Option C is incorrect because creating an Azure Synapse Spark notebook requires provisioning and managing a Spark pool, which increases administrative overhead. Option D is incorrect because using PolyBase in a dedicated SQL pool requires creating external tables and performing INSERT...SELECT, which is less flexible for reusable transformation logic compared to CETAS in serverless SQL pool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure Data Factory with Mapping Data Flows to read Parquet files, perform aggregations, and write to the dedicated SQL pool.
Why it's wrong here
Mapping Data Flows run on Spark, requiring a Spark cluster and increasing complexity.
- ✓
Use a serverless SQL pool to query the Parquet files via OPENROWSET, then use CETAS to write the aggregated results to the dedicated SQL pool using an external table.
Why this is correct
Serverless SQL pool can read Parquet natively, and CETAS allows writing to dedicated SQL pool via external table.
- ✗
Create an Azure Synapse Spark notebook that reads Parquet files, performs aggregation using PySpark, and writes the results to the dedicated SQL pool using the Spark Synapse connector.
Why it's wrong here
Requires provisioning and managing a Spark pool.
- ✗
Use PolyBase in a dedicated SQL pool to create external tables over the Parquet files, then use INSERT...SELECT to load aggregated data into the target table.
Why it's wrong here
Dedicated SQL pool cannot read Parquet directly; external tables require external data sources and file formats supported by PolyBase, but Parquet is not directly supported without using serverless.
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
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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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.