Interview questions
Preparing for a data engineering interview at Fractal? This page contains 21 real interview questions sourced from verified Fractal interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Fractal data engineering interviews typically focus on Cloud/Tools, SQL, and Spark/Big Data. There's a solid mix of fundamental and advanced questions, making it accessible for candidates at multiple experience levels.
Use the difficulty filters above to focus your preparation. For each question, attempt your own answer first, then compare with our expert solution. You can also practice these questions in our AI Mock Interview Coach for real-time feedback.
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Describe the process and use cases of implementing Azure Data Factory pipelines.
Explain Microsoft Fabric and its use in data integration.
Explain the difference between Azure Event Hub and Azure Service Bus.
Explain the differences between Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse.
Explain the purpose and architecture of Azure Synapse Analytics.
How does Azure Kubernetes Service (AKS) manage scaling and updates for containerized applications?
What are Azure Blueprints, and how are they different from Azure Policies?
What are Managed Identities in Azure, and how are they used in securing resources?
What is Azure Data Lake Storage (ADLS) Gen2, and how does it differ from Blob Storage?
Explain Stack vs Unstack and their use in data transformation.
What are Azure Functions Durable Functions, and how do they differ from regular Azure Functions?
Explain CTE vs Temp Table. What are the differences and use cases?
Explain Coalesce vs ISNULL. What are the differences in SQL?
Explain Triggers in SQL with examples and scenarios for use.
Explain row_number, rank, and dense_rank with examples.
How do you get new records from a table/file without a modified column? Discuss approaches like hashing or row comparison.
Share strategies for query and ETL optimization.
Explain Azure Databricks architecture and its integration with other Azure services.
How do you help stakeholders query Delta Lake tables? What tools and approaches?
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