DP-900 Describe an analytics workload on Azure Practice Question
A data engineer needs to build a data pipeline that runs daily to copy sales data from an on-premises SQL Server to Azure Synapse Analytics. Which Azure service should they use to orchestrate the pipeline?
⚠ Common exam trap
Test-takers frequently confuse Azure Data Factory with Azure Databricks or HDInsight because both can process data, but they overlook that the question specifically asks for orchestration of a scheduled copy pipeline, which is ADF's primary purpose, not a general-purpose analytics platform.
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 data integration service specifically designed to orchestrate and automate data pipelines. It supports scheduled triggers (e.g., daily runs) and provides native connectors to copy data from on-premises SQL Server (via Self-Hosted Integration Runtime) to Azure Synapse Analytics, making it the ideal tool for this ETL/ELT workload.
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 Analysis Services
Why it's wrong here
Azure Analysis Services is a fully managed platform that provides enterprise-grade tabular and multidimensional semantic models to support business intelligence reporting. It is focused on serving high-speed analytical queries to tools like Power BI and Excel, not on moving or transforming data in an automated pipeline. As a result, it has no built-in triggers, activities, or data integration engine, so it cannot build or run a daily data pipeline.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory is a cloud-based ETL and data integration service purpose-built for orchestrating data pipelines. It provides a code-first or UI-based authoring environment with activities, control flow, and triggers to schedule runs daily or event-driven, while handling dependencies and error retries. Additionally, its rich set of copy activities and linked services let it move and transform data from multiple sources, making it the correct choice for the engineer's requirement.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics and machine learning platform optimized for collaborative data engineering and interactive data science workloads. Although it can perform powerful data transformations, it does not offer native global-scale orchestration with scheduling triggers and cross-service dependency management like a dedicated integration tool. Databricks is better suited as a compute target within an orchestrated workflow, such as an Azure Data Factory activity, rather than as the pipeline orchestrator itself.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed Apache Hadoop, Spark, HBase, and Kafka service designed for large-scale data processing and batch compute jobs, not for workflow orchestration. It lacks native pipeline scheduling, dependency management, or built-in connectors to move data across diverse sources and sinks. While HDInsight can be invoked as a compute activity inside a pipeline, it cannot itself coordinate the end-to-end data flow, making it a component rather than the orchestrator.
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Related to this question
Learn chapter
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 Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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