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Domain 2.2: Data IntegrationDomain 2Data FactoryDatabricksData Integration

AZ-305 · Question 21 · Domain 2.2: Data Integration

Your company is building a modern data warehouse. You need to ingest 5 TB of raw JSON data daily from an external REST API, perform complex machine learning transformations on the data, and load the cleansed data into an Azure Synapse Analytics dedicated SQL pool.

The data engineering team prefers writing transformations in Python and Scala. The orchestration team prefers a visual, drag-and-drop interface for scheduling and monitoring the end-to-end pipeline.

Which TWO services should you combine to meet these requirements? (Select TWO)

Answer options:

A.

Azure Data Factory

B.

Azure Databricks

C.

Azure Stream Analytics

D.

Azure Logic Apps

E.

Azure Functions

How to approach this question

Identify the service for visual orchestration (ADF) and the service for big data Python/Scala transformations (Databricks).

Full Answer

This is a classic modern data warehouse pattern. Azure Data Factory (ADF) is used as the orchestrator because it provides a visual, drag-and-drop interface for scheduling, monitoring, and moving data (Copy Activity). For the complex transformations requiring Python and Scala, ADF can trigger an Azure Databricks notebook activity. Databricks provides a highly optimized Apache Spark environment perfect for big data machine learning transformations.

Common mistakes

Trying to use Data Factory Mapping Data Flows for the transformations. While visual, it doesn't meet the requirement of the engineering team wanting to write Python/Scala code.

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