Which Azure Service Best Supports Power Query Pipelines with Git Repositories?
Short answer
C Azure Data Factory
Question
Key Takeaways
The question tests recognition of Azure Data Factory’s native DevOps and integration runtime capabilities, with the common trap being the selection of Synapse Analytics due to perceived feature overlap.
Azure Data Factory provides native Power Query support for building and orchestrating data pipelines between on-premises sources and cloud storage. The community overwhelmingly endorses this option due to its seamless integration with self-hosted runtimes and dedicated Git version control.
Common mistake: Azure Synapse Analytics is frequently selected by candidates assuming its broader analytics suite applies, but it introduces unnecessary licensing costs and complex setup for standalone Power Query ETL workflows.
Expert Analysis
Why the Answer Is Correct
Azure Data Factory natively supports Power Query for data transformations and automated copying across hybrid environments. It allows multiple pipeline projects to be version-controlled in separate Git repositories, directly satisfying the isolation requirement. Furthermore, ADF securely bridges on-premises databases and Azure Data Lake Storage through a self-hosted integration runtime, minimizing manual configuration.
Why the Other Options Are Wrong
Azure Synapse Analytics includes pipeline capabilities but is engineered for enterprise-scale data warehousing and advanced analytics, making it excessive for this specific scope. Azure Logic Apps specializes in workflow automation and API-triggered events rather than structured data engineering pipelines. Power BI focuses exclusively on visualization and reporting, lacking native orchestration, Git repository management, and integration runtime architecture.
Community Comment Notes
The voting distribution and feedback strongly validate Azure Data Factory as the definitive solution. Comment [1] emphasizes ADF’s cloud-native integration framework and direct compatibility with Power Query for cross-environment data movement. Comments [2] and [3] confirm industry alignment and note that official documentation corroborates this architectural pattern for multi-repo DevOps workflows.
Official Reference
- https://learn.microsoft.com/en-us/azure/data-factory/
- https://learn.microsoft.com/en-us/azure/data-factory/source-control
These links open the official vendor documentation in a new tab.
Exam Strategy
When evaluating data platform options, map each requirement directly to native service features rather than overlapping capabilities. Prioritize Azure Data Factory for scenarios emphasizing Git version control, self-hosted integration runtimes, and minimal development overhead to avoid over-engineering your solution.
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 3 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 3
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Happynewyear1001 👍 3 Answered CTo develop data pipeline projects that use Microsoft Power Query and meet the specified requirements, you should use Azure Data Factory (option C). Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines that can move data between supported on-premises and cloud-based data stores. With Azure Data Factory, you can use Power Query to transform and prepare your data before moving it to the destination. It supports the use of self-hosted integration runtimes for on-premises data movement.
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warre 👍 1correct, chat gpt confirmed
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jsav1 👍 1 Answered Ccorrect
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