- A
Azure Synapse Pipelines
Synapse Pipelines are built on ADF and provide similar capabilities.
- B
Azure Data Factory
ADF provides data orchestration and transformation.
- C
Azure Logic Apps
Why wrong: Logic Apps is for business process workflows.
- D
Azure Databricks
Why wrong: Databricks is a compute platform, not an orchestrator.
- E
Azure Functions
Why wrong: Functions is a compute service for event-driven code.
Question 547 of 851
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following Azure services can be used to orchestrate data pipelines that include data transformation?
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
Both Azure Synapse Pipelines (option A) and Azure Data Factory (option B) are correct because they serve as native orchestration services for building, scheduling, and monitoring data pipelines that include data transformation activities. Azure Synapse Pipelines, built on the same technology as Azure Data Factory, provides integrated orchestration within Synapse Analytics, supporting Data Flows and other transformations. Azure Data Factory is a standalone cloud-based ETL and data integration service that can orchestrate complex pipelines involving data transformation, such as mapping data flows, stored procedures, or custom activities. In contrast, Azure Logic Apps (C) is designed for workflow automation and integration across SaaS applications, not for heavy data transformation or pipeline orchestration. Azure Databricks (D) is an analytics platform used primarily for big data processing and machine learning, often serving as a compute target within a pipeline rather than an orchestrator. Azure Functions (E) is a serverless compute service for event-driven code execution, typically used as a custom activity within a pipeline, not as the orchestrator itself. Therefore, the two services that can orchestrate data pipelines with transformation are Azure Synapse Pipelines and Azure Data Factory.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 are built on ADF and provide similar capabilities.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Azure Data Factory
Why this is correct
ADF provides data orchestration and transformation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Logic Apps
Why it's wrong here
Logic Apps is for business process workflows.
- ✗
Azure Databricks
Why it's wrong here
Databricks is a compute platform, not an orchestrator.
- ✗
Azure Functions
Why it's wrong here
Functions is a compute service for event-driven code.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, both Azure Synapse Pipelines and Azure Data Factory use the same Integration Runtime (IR) to connect to data sources and execute activities, including Mapping Data Flows that run on Spark clusters for transformation. A subtle behavior is that Synapse Pipelines can directly reference Synapse SQL pools or Spark pools as compute targets, while Data Factory requires linked services to connect to external compute like Databricks or HDInsight. In a real-world scenario, you might use Synapse Pipelines to orchestrate a pipeline that ingests raw data from Blob Storage, transforms it using a Data Flow, and loads it into a dedicated SQL pool—all within a single managed environment.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 |
What to study next
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FAQ
Questions learners often ask
What does this DP-203 question test?
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Synapse Pipelines — Both Azure Synapse Pipelines (option A) and Azure Data Factory (option B) are correct because they serve as native orchestration services for building, scheduling, and monitoring data pipelines that include data transformation activities. Azure Synapse Pipelines, built on the same technology as Azure Data Factory, provides integrated orchestration within Synapse Analytics, supporting Data Flows and other transformations. Azure Data Factory is a standalone cloud-based ETL and data integration service that can orchestrate complex pipelines involving data transformation, such as mapping data flows, stored procedures, or custom activities. In contrast, Azure Logic Apps (C) is designed for workflow automation and integration across SaaS applications, not for heavy data transformation or pipeline orchestration. Azure Databricks (D) is an analytics platform used primarily for big data processing and machine learning, often serving as a compute target within a pipeline rather than an orchestrator. Azure Functions (E) is a serverless compute service for event-driven code execution, typically used as a custom activity within a pipeline, not as the orchestrator itself. Therefore, the two services that can orchestrate data pipelines with transformation are Azure Synapse Pipelines and Azure Data Factory.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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