DP-900 Describe an analytics workload on Azure Practice Question

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 dedicated cloud ETL orchestration service that natively supports scheduled execution via triggers (e.g., hourly tumbling window triggers) and a Copy Activity to move data from on-premises or cloud sources to destinations like Azure Synapse Analytics. It also orchestrates complex pipelines by chaining activities, such as running a Databricks notebook for PySpark transformations, all within a single monitored workflow. This native scheduling and data-movement capability is exactly what a pipeline that 'runs every hour and copies new sales data' requires.

• ✗

  Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is built for continuous, real-time stream processing—consuming data from sources like Event Hubs or IoT Hub and applying windowing, aggregations, and alerts on data in motion. It does not provide a batch orchestration model with a scheduled hourly trigger for copying static, persisted data. Its purpose is to analyze live streams, not to move or transform large sets of stored data on a recurring batch schedule, and it lacks the Copy Activity and pipeline orchestration features found in Azure Data Factory.

• ✗

  Azure Logic Apps

  Why it's wrong here

  Azure Logic Apps is a low-code workflow automation platform focused on integrating Software-as-a-Service applications via hundreds of connectors, such as Outlook, SharePoint, or Dropbox, and can trigger hourly via recurrence triggers. However, it lacks a built-in data movement engine for copying large volumes of sales data between databases or data warehouses, and its support for big data compute steps like PySpark on Databricks is not native or performant. Complex ETL pipelines require controlled data transfer and integration with monitoring and compute services, which Logic Apps is not designed to provide.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks is an analytics compute platform that can execute PySpark notebooks for data transformation, but it is not an orchestration or data-movement service. While Databricks can schedule notebook jobs, it does not natively provide a Copy Activity to transfer data from sources like SQL Server to Synapse, nor does it manage cross-step dependencies involving other Azure services. A pipeline that runs hourly and copies data typically needs Data Factory to orchestrate the overall process, with Databricks serving as one of the compute steps for transformation, rather than replacing the orchestrator itself.

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