DP-203 Develop data processing Practice Question
Your company uses Azure Data Lake Storage Gen2 as the central data lake. You need to process batch data using serverless Spark jobs that can be scheduled daily. Which Azure service should you use?
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
✓
Azure Synapse Analytics serverless Spark pool with pipelines.
Azure Synapse Analytics provides serverless Spark pools with built-in scheduling via pipelines, allowing you to run daily batch jobs without managing clusters. Option A (Azure Batch) is for custom compute workloads, not Spark jobs. Option C (Azure Databricks) requires a job cluster that is not serverless. Option D (Azure Machine Learning) is designed for ML workflows, not general batch data processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Batch with custom Spark containers.
Why it's wrong here
Azure Batch is not optimized for Spark and adds complexity.
- ✓
Azure Synapse Analytics serverless Spark pool with pipelines.
Why this is correct
Synapse provides serverless Spark pools with automatic scaling and built-in scheduling via pipelines.
- ✗
Azure Databricks with a job cluster.
Why it's wrong here
Databricks requires cluster management and is not fully serverless in the same way as Synapse serverless Spark.
- ✗
Azure Machine Learning with Spark compute.
Why it's wrong here
Azure ML is designed for ML experiments, not general batch processing.
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 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.
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.
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