Quick Answer
The correct answer is Azure Data Factory, as it is the dedicated cloud-based ETL and data integration service purpose-built for orchestrating and automating batch data pipelines. Azure Data Factory excels here because it provides native connectors for both FTP sources and Azure Data Lake Storage sinks, along with built-in transformation activities like Copy Data and Mapping Data Flows that can convert CSV files into Parquet format. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of which service handles scheduled, dependency-driven orchestration versus services like Azure Databricks or Synapse, which focus more on compute-intensive processing or analytics. A common trap is choosing Azure Synapse Analytics because it also handles data movement, but Synapse is a broader analytics platform, not a pure orchestration service. Remember the memory tip: “ADF is the conductor, not the musician”—it orchestrates the pipeline steps, while other services perform the heavy lifting.
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.
You are designing a batch processing pipeline that runs nightly to transform CSV files from an FTP server into Parquet files in Azure Data Lake Storage. Which Azure service should you use to orchestrate the pipeline?
Azure Functions
Why wrong: Functions are for event-driven compute, not orchestration.
Azure Data Factory
ADF provides orchestration for batch data pipelines with transformations.
Azure Logic Apps
Why wrong: Logic Apps are for simpler integration workflows, not complex data pipelines.
Azure Batch
Why wrong: Batch is for running large-scale parallel jobs, not pipeline orchestration.
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 is a cloud-based ETL and data integration service designed specifically for orchestrating and automating data pipelines. It supports scheduled triggers (e.g., nightly runs), native connectors for FTP and Azure Data Lake Storage, and built-in data transformation activities like Copy Data and Mapping Data Flows to convert CSV to Parquet. ADF's control flow and dependency management make it ideal for batch processing pipelines.
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 Functions
Why it's wrong here
Functions are for event-driven compute, not orchestration.
Azure Data Factory
Why this is correct
ADF provides orchestration for batch data pipelines with transformations.
Related concept
Read the scenario before looking for a memorised answer.
Azure Logic Apps
Why it's wrong here
Logic Apps are for simpler integration workflows, not complex data pipelines.
Azure Batch
Why it's wrong here
Batch is for running large-scale parallel jobs, not pipeline orchestration.
Common exam traps
The trap here is that candidates confuse Azure Data Factory with Azure Logic Apps or Azure Functions, assuming any 'automation' or 'serverless' service can orchestrate a batch ETL pipeline, but only ADF provides the native data movement, transformation, and scheduling capabilities required for this specific scenario.
Detailed technical explanation
Under the hood, Azure Data Factory uses a JSON-based pipeline definition language and a self-hosted integration runtime to connect to on-premises FTP servers securely. It leverages PolyBase or COPY INTO for high-throughput data loading into Azure Data Lake Storage, and its Mapping Data Flows execute Spark-based transformations at scale without managing clusters. A real-world scenario is a retail company ingesting nightly sales CSVs from an FTP server, transforming them into Parquet for optimized querying in Azure Synapse Analytics, with ADF handling retries, logging, and monitoring.
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 is a cloud-based ETL and data integration service designed specifically for orchestrating and automating data pipelines. It supports scheduled triggers (e.g., nightly runs), native connectors for FTP and Azure Data Lake Storage, and built-in data transformation activities like Copy Data and Mapping Data Flows to convert CSV to Parquet. ADF's control flow and dependency management make it ideal for batch processing pipelines.
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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