DP-203 Develop data processing Practice Question
You are designing a data processing solution that requires running custom Python scripts for data transformation. The scripts have dependencies on specific libraries that are not pre-installed in the environment. You need a fully managed, serverless compute option that allows you to install custom libraries. Which service should you use?
⚠ Common exam trap
Many exam-takers confuse serverless compute with any service that can run code, but Azure Functions lacks native support for installing arbitrary Python libraries without custom containerization, while Azure Databricks provides a purpose-built, managed environment for data processing with easy library management.
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 Databricks with cluster-scoped libraries
Azure Databricks with cluster-scoped libraries is the correct choice because it provides a fully managed, serverless Apache Spark environment where you can install custom Python libraries (e.g., via PyPI or Maven) at the cluster level. This allows your custom Python scripts to run with all required dependencies without managing any infrastructure, directly meeting the requirement for a serverless compute option with custom library support.
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 Databricks with cluster-scoped libraries
Why this is correct
Databricks allows installing libraries at cluster level and is fully managed.
- ✗
Azure Functions with custom handlers
Why it's wrong here
Azure Functions can run Python but have limitations on library installation and execution time.
- ✗
Azure Container Instances with a custom Docker image
Why it's wrong here
Container Instances are not fully managed in the same way as Databricks and require container image management.
- ✗
Azure Synapse Analytics serverless SQL pool
Why it's wrong here
Serverless SQL pool is for SQL queries, not Python scripts.
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
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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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.