DP-203 Develop data processing Practice Question
You are a data engineer at a retail company. You need to design a batch processing solution that ingests daily sales data from multiple stores. Each store uploads a CSV file to a dedicated folder in Azure Data Lake Storage Gen2. The files have the same schema but may have minor variations in column order and include null values. After ingestion, you must clean the data by removing rows with null values in the 'SalesAmount' column, convert the 'Date' column from string to date type, and aggregate sales by product category. The output should be stored as Parquet files partitioned by year and month in the same Data Lake. You need to choose a compute service and implement the transformation with minimal coding effort. The solution must be cost-effective and require no cluster management. What should you do?
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 Azure Data Factory with a Mapping Data Flow. Configure the source to read all CSV files from the ADLS Gen2 folder. Add a Filter transformation to remove null SalesAmount, a Derived Column to parse the Date, and an Aggregate to sum sales by category. Sink to ADLS Gen2 as Parquet with partition by year and month.
Azure Data Factory Mapping Data Flows provide a code-free visual interface for transformations. They can read multiple CSV files from ADLS Gen2, handle schema variations, filter nulls, parse dates, aggregate, and write partitioned Parquet files. This meets the requirements of minimal coding, cost-effectiveness (pay-per-execution), and no cluster management. A is incorrect because while Azure Synapse Serverless SQL pool can query CSV files and use CETAS to write Parquet, it requires T-SQL coding, handling schema drift is more complex, and partitioning via CETAS can be cumbersome. Also, serverless SQL is optimized for ad-hoc querying rather than batch ETL. B is incorrect because Azure Synapse Dedicated SQL pool requires provisioning and managing a dedicated cluster, incurring ongoing costs even when idle. It also involves more coding with T-SQL and PolyBase, and does not align with 'no cluster management' requirement. D is incorrect because Azure Databricks requires creating and managing a cluster, and coding in PySpark or Scala, which contradicts 'minimal coding effort' and 'no cluster management'.
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 Synapse Serverless SQL pool. Create external tables over the CSV files, write a T-SQL query to filter, cast, aggregate, and use CETAS to write Parquet partitions.
Why it's wrong here
Serverless SQL is not designed for complex transformations and partitioning may be less performant.
- ✗
Use Azure Synapse Dedicated SQL pool. Load CSV files via PolyBase, transform with T-SQL, and use CREATE TABLE AS SELECT to output partitioned Parquet.
Why it's wrong here
Requires provisioning a dedicated pool, incurring cost, and is not serverless.
- ✓
Use Azure Data Factory with a Mapping Data Flow. Configure the source to read all CSV files from the ADLS Gen2 folder. Add a Filter transformation to remove null SalesAmount, a Derived Column to parse the Date, and an Aggregate to sum sales by category. Sink to ADLS Gen2 as Parquet with partition by year and month.
Why this is correct
Mapping Data Flow provides code-free transformations and handles schema drift.
- ✗
Use Azure Databricks with a PySpark notebook. Mount the ADLS Gen2 storage, read CSV files with schema inference, filter, cast, aggregate, and write partitioned Parquet.
Why it's wrong here
Requires coding and cluster management, increasing effort and cost.
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 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.
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.