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 a managed cloud ETL service that lets you author, schedule, and monitor data movement and transformation. It uses pipelines, linked services, datasets, and a self-hosted integration runtime to copy data from an on-premises SQL Server to Azure Synapse Analytics. With code-free pipeline orchestration, schedule triggers, and comprehensive monitoring, it is a core service for building batch data pipelines.

• ✓

  Azure Databricks

  Why this is correct

  Azure Databricks is an Apache Spark-based unified analytics platform that supports data engineering and data science workloads via notebooks or automated jobs. You can use a JDBC connection to read from an on-premises SQL Server, apply scale-out transformations in Spark, and then write the results to Azure Synapse Analytics using the Synapse connector. This makes it a valid compute engine for building pipelines that move and transform data from source to destination.

• ✗

  Azure Machine Learning

  Why it's wrong here

  Azure Machine Learning is a purpose-built service for the end-to-end machine learning lifecycle, including experiment tracking, model training, hyperparameter tuning, deployment, and responsible AI. It does not provide native ETL capabilities for copying or moving structured data from on-premises systems to a cloud warehouse, nor does it orchestrate scheduled data movement between sources and targets. While you could write code to move data in a notebook, that is not its intended role, and it lacks the built-in connectors and pipeline triggers of a true data integration service.

• ✗

  Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is a real-time event processing engine designed to continuously query streaming data with sliding or tumbling windows, ingesting from sources like Event Hubs, IoT Hub, or Blob Storage. It is not built for batch movement from an on-premises SQL Server to a cloud data warehouse, and it does not have a self-hosted integration runtime for reading on-premises relational databases. Its SQL-like semantics focus on temporal computations over live data streams, not on orchestrating scheduled transfers or managing pipeline dependencies.

• ✗

  Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services provides enterprise-grade semantic models and in-memory tabular databases that serve as a presentation layer for business intelligence tools such as Power BI and Excel. It consumes data that has already been prepared by an ETL pipeline, and it does not orchestrate ingestion or job scheduling from on-premises sources. You can import data into a model, but the service lacks pipeline activities, triggers, and source-to-destination connectors, so it cannot be used to build a data pipeline.

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