Microsoft Azure Solutions Architect Expert AZ-305 Practice Question
Your organization keeps hourly clickstream JSON files in an Azure Data Lake Storage Gen2 container. Data engineers must ingest the files, flatten nested JSON arrays, perform several joins, and load the results into Azure SQL Database every hour. They want a managed, code-free service that can orchestrate the workflow, provide visual data-lineage, automatically spin up and shut down compute clusters, and integrate with Azure Monitor-while minimizing operational overhead. Which Azure service best meets these requirements?
Azure Functions triggered by Event Grid that call stored procedures to load data
An Azure Stream Analytics job that processes the JSON files with SQL-style queries
An Azure Logic Apps workflow that moves files from ADLS Gen2 to Azure SQL Database
Azure Data Factory Mapping Data Flows executed on managed Spark clusters
Azure Data Factory (ADF) Mapping Data Flows are designed for precisely this scenario. ADF offers a visual, code-free authoring experience that lets data engineers build complex transformations such as JSON flattening, joins, aggregations, and look-ups without writing Spark code. Each data-flow execution is run on an automatically provisioned, scaled-out Azure-managed Spark cluster that starts when the job begins and is torn down when the job finishes, so you pay only for the runtime. ADF pipelines supply native scheduling, dependency management, and integration with Azure Monitor for logging and alerting, while the Data Flow canvas provides lineage and monitoring views.
The other options do not fully satisfy the requirements:
Azure Stream Analytics focuses on streaming data and offers limited support for complex, batch-oriented transformations like multitable joins and JSON hierarchy flattening.
Azure Logic Apps provide workflow orchestration but lack the scalable data-transformation engine required for large-volume joins and complex data shaping.
Azure Functions would require substantial custom code for parsing, transformation, scaling, and monitoring, increasing operational overhead rather than reducing it.
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How does Azure Data Factory Mapping Data Flows handle JSON flattening?
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What makes Azure-managed Spark clusters suitable for this use case?
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How does Azure Data Factory integrate with Azure Monitor for observability?
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Microsoft Azure Solutions Architect Expert AZ-305
Design data storage solutions
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