Quick Answer
The answer is Azure Data Factory. This is the correct choice because Azure Data Factory provides native integration with both Azure Blob Storage and Azure Synapse Analytics, and its mapping data flows offer a visual, code-free designer for transforming data at scale on Spark clusters, making it the ideal ETL service for daily ingestion and transformation workloads. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of which Azure service orchestrates data movement and transformation between storage and analytics layers; a common trap is confusing Azure Data Factory with Azure Synapse Pipelines, but remember that Synapse Pipelines are actually built on top of ADF, so ADF remains the core ETL service. A helpful memory tip: think of ADF as the "factory" that builds and moves your data products, while Synapse is the "warehouse" where they are stored and analyzed.
This DP-900 practice question tests your understanding of describe core data concepts. 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.
Your organization has a data warehouse in Azure Synapse Analytics. You need to load data from Azure Blob Storage daily, transforming it using a data flow. Which Azure service should you use for the ETL process?
Azure Databricks
Why wrong: Requires code (Spark) for transformations, not a visual data flow.
Azure Data Factory
Offers mapping data flows for visual ETL without coding.
Azure Logic Apps
Why wrong: Best for simple workflows and integrations, not complex data transformations.
Azure Synapse Pipelines
Why wrong: Synapse Pipelines are built on Azure Data Factory, but the core service is Data Factory.
Answer choices
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
Azure Data Factory (ADF) is the correct choice because it provides native integration with Azure Synapse Analytics and Azure Blob Storage, and it includes a visual data flow designer for transforming data without writing code. ADF's mapping data flows execute at scale on Spark clusters, making it ideal for daily ETL workloads that require both ingestion and transformation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Databricks
Why it's wrong here
Requires code (Spark) for transformations, not a visual data flow.
Azure Data Factory
Why this is correct
Offers mapping data flows for visual ETL without coding.
Related concept
Read the scenario before looking for a memorised answer.
Azure Logic Apps
Why it's wrong here
Best for simple workflows and integrations, not complex data transformations.
Azure Synapse Pipelines
Why it's wrong here
Synapse Pipelines are built on Azure Data Factory, but the core service is Data Factory.
Common exam traps
The trap here is that candidates confuse Azure Synapse Pipelines (which is just ADF inside Synapse) as a separate service, but the correct Azure service name for the ETL tool is Azure Data Factory, not Synapse Pipelines.
Detailed technical explanation
Under the hood, Azure Data Factory mapping data flows compile into optimized Spark jobs that run on Azure Databricks clusters managed by ADF, allowing for schema drift handling and partition optimization. ADF supports PolyBase for high-throughput loading into Synapse dedicated SQL pools, and it can trigger pipelines on a schedule or via blob storage events for near-real-time ingestion. A real-world scenario is a retail company that uses ADF to read daily sales CSV files from Blob Storage, apply cleansing and aggregation transformations in a data flow, and then bulk insert the results into a Synapse table using the Copy activity with PolyBase.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Data Factory — Azure Data Factory (ADF) is the correct choice because it provides native integration with Azure Synapse Analytics and Azure Blob Storage, and it includes a visual data flow designer for transforming data without writing code. ADF's mapping data flows execute at scale on Spark clusters, making it ideal for daily ETL workloads that require both ingestion and transformation.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DP-900 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-900 exam.
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