DP-900 Describe an analytics workload on Azure Practice Question
Which TWO Azure services can be used to orchestrate and automate data pipelines? (Choose two.)
⚠ Common exam trap
Many candidates confuse Azure Databricks (which can run data transformation code) with a pipeline orchestration service, but it lacks the native scheduling, dependency management, and visual pipeline designer that Azure Data Factory and Synapse Pipelines provide.
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 Synapse Pipelines
Azure Data Factory (E) is a dedicated cloud-based ETL and data integration service that allows you to create, schedule, and orchestrate data pipelines at scale. Azure Synapse Pipelines (B) is built on the same engine as Azure Data Factory and provides native pipeline orchestration within the Synapse Analytics workspace, enabling you to move and transform data across various sources and sinks. Both services offer visual design tools, code-free transformations, and robust scheduling capabilities for automating data workflows.
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 SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database service, not a pipeline orchestration tool. It stores and queries relational data but does not have built-in capabilities to define, schedule, or monitor data movement/transformation workflows. Even with SQL Agent jobs for scheduling, it lacks the comprehensive integration and activity orchestration of a dedicated ETL service.
- ✓
Azure Synapse Pipelines
Why this is correct
Azure Synapse Pipelines provide cloud-scale data integration and orchestration within the Azure Synapse Analytics workspace. They are built on the same engine as Azure Data Factory, allowing you to create and schedule data-driven pipelines to ingest, transform, and publish data across hybrid and multi-cloud environments.
- ✗
Power BI
Why it's wrong here
Power BI is a business analytics and visualization platform, not an orchestration service. It creates interactive reports and dashboards but does not natively execute or coordinate data movement between sources and destinations. While it can integrate with data sources and refresh datasets, it lacks the pipeline activities, control flow, and scheduling granularity of a dedicated orchestration tool.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform for big data engineering and machine learning. It can run notebooks and jobs that process data, but it is not a data integration orchestration service. Orchestrating pipelines across multiple activities, dependencies, and data stores is not its core function; rather, it serves as a compute engine that other tools like Data Factory can invoke.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory is a hybrid data integration service that lets you create, schedule, and orchestrate data pipelines. It supports over 90 built-in connectors, control flow activities, and triggers to automate ETL/ELT processes across on-premises and cloud sources. Its monitoring and alerting capabilities make it a primary choice for data pipeline orchestration in Azure.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
ETL
ETL stands for Extract, Transform, Load, a process that moves data from multiple source systems into a single database, data warehouse, or data lake for analysis and reporting.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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