DP-203 Develop data processing Practice Question
You are developing a data processing solution that requires aggregating sales data from multiple CSV files stored in Azure Data Lake Storage Gen2. The data should be cleansed and transformed before loading into Azure Synapse Analytics. Which Azure service should you use to implement a code-free transformation pipeline?
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 Data Factory with Mapping Data Flows
Azure Data Factory with Mapping Data Flows allows code-free visual transformations. Azure Databricks and HDInsight require code. Azure Analysis Services is for tabular modeling, not 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 HDInsight with Hive
Why it's wrong here
Azure HDInsight with Hive demands HiveQL scripting and cluster provisioning, so it is not code-free; it also targets big-data batch processing rather than visual pipeline authoring. It tempts because it can aggregate and transform CSV data at scale, and would fit scenarios where open-source Hadoop tooling and custom queries are required.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services provides semantic tabular modelling and query serving over already-prepared data; it cannot ingest multiple CSV files or perform code-free cleansing and transformation. It tempts because it sits downstream in analytics architectures, but the requirement is a transformation pipeline, which Azure Data Factory or Synapse pipelines deliver.
- ✓
Azure Data Factory with Mapping Data Flows
Why this is correct
Mapping Data Flows provide a visual, code-free canvas for joins, aggregations and transformations that run on Spark clusters. This satisfies the code-free transformation requirement while reading CSV files from Data Lake Storage Gen2 and loading Synapse.
- ✗
Azure Databricks with PySpark
Why it's wrong here
Azure Databricks with PySpark requires writing code, directly contradicting the code-free transformation requirement; it also needs cluster management rather than drag-and-drop pipeline authoring. It tempts because it handles large-scale cleansing and aggregation well, and would suit scenarios where custom code and notebook-based engineering are acceptable.
Go deeper
Related to this question
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Implement Azure Stream Analytics
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 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.
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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.