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 for orchestrating scheduled batch loading from ADLS Gen2 into a Synapse dedicated SQL pool. Its Copy activity provides high-throughput, parallelized data movement with native connectors for both ADLS Gen2 and Azure Synapse, while pipeline triggers (such as schedule or tumbling window) enable reliable recurring execution. ADF also offers robust error handling, retries, and monitoring, and can invoke Stored Procedure activities to run Synapse transformations, making it the definitive ETL orchestration service for this scenario.

• ✗

  Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is a real-time stream processing engine that ingests data from sources like Event Hubs or IoT Hub and applies continuous SQL-based queries over tumbling, hopping, or sliding time windows. It is not designed for scheduled batch ingestion of files from a data lake: it cannot natively read a static file from ADLS Gen2 on a recurring schedule and load it into a Synapse dedicated SQL pool, nor does it provide pipeline orchestration, retry logic, or monitoring for batch ETL jobs. Its transient, in-memory processing model is fundamentally mismatched for persistent, file-based data warehouse loading.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks is an Apache Spark-based analytics platform that excels at large-scale data transformations and machine learning using Python, Scala, or SQL in collaborative notebooks, but it lacks native pipeline orchestration and scheduling capabilities for end-to-end ETL workflows. While you can write Spark code to read from ADLS Gen2 and write to a Synapse dedicated SQL pool using the spark-synapse connector, you would need an external orchestrator like Azure Data Factory or Synapse Pipelines to trigger the job on a schedule and manage dependencies, retries, and alerts. Without such orchestration, Databricks alone is not the appropriate tool for a scheduled, production-grade data warehouse loading pipeline.

• ✗

  Azure Logic Apps

  Why it's wrong here

  Azure Logic Apps is a low-code integration service intended to automate workflows between applications and services, such as sending email notifications, syncing records between SaaS apps, or processing Office 365 events, using prebuilt connectors. While it can respond to file creation events in ADLS Gen2 and call Azure Synapse SQL APIs, it lacks the data-plane performance, parallel partitioning, and built-in monitoring needed to move large volumes of data efficiently into a dedicated SQL pool. Logic Apps is optimized for lightweight application-to-application integration, not for heavy, sustained data warehouse ETL operations, making it unsuitable as the primary data loading solution.