DP-203 Develop data processing Practice Question
You are designing a batch processing solution for a financial services company that processes transactions from multiple sources. The data is stored in Azure Data Lake Storage Gen2 in Parquet format. You need to perform complex transformations including joins, aggregations, and window functions, and then load the results into an Azure Synapse Analytics dedicated SQL pool. The transformations must be written in SQL and executed on a serverless Spark cluster to minimize costs. You want to manage the code in a Git repository and automate the deployment using Azure DevOps. 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
✓
Create a Spark job definition in Azure Synapse that reads Parquet files, performs transformations using PySpark, and writes to the dedicated SQL pool using the Spark Synapse connector.
Using a Spark job definition in Azure Synapse allows you to run PySpark transformations on a serverless Spark cluster, which meets the requirement for SQL-based transformations via Spark SQL. The Spark Synapse connector enables efficient writing to a dedicated SQL pool. This approach minimizes costs by leveraging serverless Spark, and the code can be managed in Git and deployed with Azure DevOps. Option A uses Azure Databricks, which is a separate service not integrated with Synapse serverless Spark. Option B uses Mapping Data Flows, which runs on Spark but is not a serverless Spark cluster in the same sense and may incur higher costs. Option D uses a serverless SQL pool, which is not a Spark cluster and thus does not fulfill the requirement to execute on a serverless Spark cluster.
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 Databricks with Auto Loader to read from Data Lake Storage, perform transformations using Spark SQL, and write to the dedicated SQL pool via JDBC.
Why it's wrong here
Incorrect. Azure Databricks with Auto Loader uses a Spark cluster, but it is not a serverless Spark cluster within Azure Synapse. It also incurs additional costs and does not integrate natively with Synapse serverless Spark.
- ✗
Use Azure Data Factory with Mapping Data Flows to visually design transformations and write to the dedicated SQL pool.
Why it's wrong here
Incorrect. Azure Data Factory Mapping Data Flows run on Spark, but they are not executed on a serverless Spark cluster. They are visual tools that may be less flexible for complex transformations and cost more than necessary.
- ✓
Create a Spark job definition in Azure Synapse that reads Parquet files, performs transformations using PySpark, and writes to the dedicated SQL pool using the Spark Synapse connector.
Why this is correct
Correct. A Spark job definition in Azure Synapse runs on a serverless Spark cluster, uses PySpark (including Spark SQL) for complex transformations, writes to a dedicated SQL pool via the Synapse connector, and integrates with Git and Azure DevOps.
- ✗
Use a serverless SQL pool in Azure Synapse to query the Parquet files using T-SQL, then use CETAS to write the results to the dedicated SQL pool.
Why it's wrong here
Incorrect. While a serverless SQL pool can query Parquet files and use CETAS to write to the dedicated SQL pool, it is not a Spark cluster. The requirement explicitly demands execution on a serverless Spark cluster, so this option does not meet that condition.
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 Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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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