Which Azure Service Best Supports Power Query Pipelines with Git Repositories?
You have an on-premises database named db1 and a set-hosted integration runtime. You have an Azure subscription that contains an Azure Data Lake Storage account named dl1. You need to develop four data pipeline projects that will use Microsoft Power Query to copy data from db1 to dl1. The solution must meet the following requirements: • All pipelines must use the self-hosted integration runtime. • Each project must be stored in a separate Git repository. • Development effort must be minimized. What should you use?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
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.
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.
Community Discussion (3 comments)
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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
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.