DP-900 Describe an analytics workload on Azure Practice Question
A retail company receives daily sales data as CSV files in Azure Data Lake Storage Gen2. They need to load this data into an Azure Synapse Analytics dedicated SQL pool every night. The process must be automated, scheduled, and include error handling for failed loads. Which Azure service should they use to orchestrate this pipeline?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Data Factory with Azure Logic Apps because both can schedule and trigger actions, but Logic Apps is designed for lightweight API integrations and lacks the native data movement capabilities and PolyBase support required for bulk loading 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 integration service designed specifically for orchestrating and automating data pipelines. It supports scheduled triggers, can copy CSV files from Azure Data Lake Storage Gen2 into an Azure Synapse dedicated SQL pool, and provides built-in error handling via retry policies, activity-level error outputs, and pipeline failure notifications.
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 correct choice because it is a cloud-based ETL and data-integration service designed specifically for orchestrating and automating batch pipelines. Its Copy Activity can reliably move CSV files from Azure Data Lake Storage into a dedicated SQL pool in Azure Synapse Analytics, while a scheduled trigger (e.g., daily recurrence) handles the nightly cadence. Data Factory also provides native error handling—such as retry policies, activity-level logging, and failure alerts—that are essential for unattended batch loads. This makes it the purpose-built service for this scenario, unlike generic compute or workflow tools.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is incorrect because it is a real-time stream-processing engine, not a batch scheduler. It continuously consumes data from streaming sources like Event Hubs or IoT Hubs through a job that never stops; it cannot be instructed to 'pick up a file at 2 AM' and load it as a one-time batch. While Stream Analytics can output to Azure Synapse, its data source must be a live stream, and static CSV files in storage are treated as a stream of individual file blobs only when they arrive as events—not as a scheduled, batched copy with transactional error handling. Thus, it lacks the scheduling, retry, and file-level orchestration capabilities required here.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks can process data and write to Synapse, but it is a compute engine, not a scheduling and orchestration service. It can be used within a pipeline orchestrated by Data Factory, but alone it lacks native scheduling and error-handling capabilities for simple file loads.
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps lacks native integration with Azure Synapse Analytics dedicated SQL pools for scheduled data loading, as it is designed for lightweight API orchestration and event-driven workflows rather than large-scale data movement. It is tempting because Logic Apps excels at automating simple file-handling tasks, such as copying a CSV from one location to another, and would be correct if the requirement were only to trigger a notification or move a file upon arrival, not to load data into a dedicated SQL pool with error handling.
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Data Roles and Core Concepts
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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