Question 904 of 820
DP-900 Describe an analytics workload on Azure Practice Question
A company uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every night, they need to load 500 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2. The loading process must be automated, scheduled, and include error handling (e.g., skip corrupt rows and log them). Which Azure service should be used to orchestrate this load pipeline?
⚠ Common exam trap
Many exam-takers confuse Azure Data Factory with Azure Logic Apps because both support scheduling and automation, but Logic Apps lacks the native data movement capabilities and fault tolerance for large-scale batch ETL workloads like loading 500 GB into a dedicated SQL pool.
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
Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service that supports scheduled execution, error handling (e.g., skipping corrupt rows via fault tolerance settings in the Copy activity), and native integration with Azure Data Lake Storage Gen2 and Azure Synapse dedicated SQL pool. ADF can automate the nightly 500 GB load using a trigger, and its mapping data flows or Copy activity can log errors to a separate file or table, meeting the requirement for automated, scheduled, and error-tolerant ingestion.
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 Data Factory
Why this is correct
Azure Data Factory is the intended ETL/ELT orchestration service for this workload. A scheduled trigger can launch a copy activity that uses the dedication SQL pool's COPY/PolyBase path for high-throughput ingestion of 500 GB, while the Azure Integration Runtime scales to handle large data volumes and provides built-in retry, monitoring, and custom error handling.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is designed for near-real-time processing of unbounded event streams from sources like Event Hubs or IoT Hub, not for scheduled batch loads of large files. It has no copy activity or file-level scheduling, and its analytical queries emit streaming results rather than performing a one-time bulk insert of gigabytes into a dedicated SQL pool.
- ✗
Azure HDInsight
Why it's wrong here
HDInsight offers managed open-source clusters such as Spark or Hive that could transform and write data, but it is not a serverless orchestration pipeline. To move 500 GB daily you would need to provision and manage a cluster, write explicit Spark/Hive code for scheduling, partitioning, and error handling, and tolerate cold-start or persistent compute costs—none of which Data Factory's native pipeline design requires.
- ✗
Azure Logic Apps
Why it's wrong here
Logic Apps is a low-code integration service for connecting APIs, SaaS apps, and event-driven workflows; its schedule triggers are meant for lightweight, connector-based actions. The connectors invoke REST/API calls that can time out on transfers of hundreds of gigabytes, and Logic Apps lacks the high-performance parallel copy engine, retry polices, and COPY/ PolyBase optimization needed to load 500 GB efficiently into a Synapse dedicated SQL pool.
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Last reviewed: Jun 11, 2026
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