DP-203 Design and implement data storage Practice Question
Which TWO of the following Azure services can be used to orchestrate data pipelines that include data transformation?
⚠ Common exam trap
Many candidates confuse compute services (like Databricks or Functions) with orchestration services, mistakenly thinking they can replace Azure Data Factory or Synapse Pipelines for end-to-end pipeline management, when in fact they are typically used as activities within an orchestrated 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 Synapse Pipelines
Azure Synapse Pipelines (A) is correct because it is the built-in pipeline orchestration engine in Azure Synapse Analytics, providing the same Data Factory-style activities (Copy, Data Flow, Stored Procedure, Notebook) that let you schedule and orchestrate data movement with transformation steps such as Mapping Data Flows. Azure Data Factory (B) is correct because it is Azure's dedicated cloud ETL/ELT orchestration service, where pipelines chain activities like Copy, Mapping Data Flow, Databricks notebook, and stored procedure activities to move and transform data. Azure Logic Apps (C) is a workflow/automation service for app and system integration, not a data pipeline orchestrator with transformation activities. Azure Databricks (D) is an Apache Spark analytics/compute platform that performs transformations but is typically invoked as an activity within a pipeline rather than being the orchestrator itself. Azure Functions (E) is a serverless compute service for running event-driven code, not a data pipeline orchestration service.
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 Synapse Pipelines
Why this is correct
Synapse Pipelines provides orchestration with built-in data transformation activities, including Mapping Data Flows and notebook execution, within the Synapse workspace. It schedules and coordinates pipeline activities, satisfying the requirement to orchestrate pipelines that include transformation.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory orchestrates pipelines and natively invokes transformation compute, satisfying the stem's requirement for both scheduling and transformation. Its Mapping Data Flows execute transformations at scale on Spark, while activities can call Databricks, HDInsight or stored procedures, letting one pipeline coordinate ingestion and transformation without external schedulers.
- ✗
Azure Logic Apps
Why it's wrong here
Logic Apps orchestrates workflows through connectors and triggers, but it lacks native data transformation activities for large-scale pipelines; transformation would require external compute. It is tempting because it schedules and chains steps well, which suits application and system integration scenarios rather than Azure Data Factory or Synapse pipeline workloads.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark analytics platform for transformation and notebooks, not a pipeline orchestrator; it lacks the scheduling, dependency, and monitoring constructs of Data Factory or Synapse pipelines. Databricks is tempting because it processes data at scale, and pipelines commonly call it as an activity, but it does not orchestrate them.
- ✗
Azure Functions
Why it's wrong here
Azure Functions executes event-driven code, but it is compute rather than an orchestration engine; it cannot schedule, chain, and monitor pipeline activities. Functions are tempting because Data Factory and Synapse pipelines invoke them for custom transformation, yet the stem asks which services orchestrate pipelines, not which perform the transformation.
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
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Implement Azure Stream Analytics
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.
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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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.