Real interview questions asked at Fractal. Practice the most frequently asked questions and land your next role.
Fractal data engineering interviews test your ability across multiple domains. These questions are sourced from real Fractal interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 11 easy, 3 medium, and 7 hard questions. Recurring themes are sql, partition, and optimization — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at KPMG and Matrix, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 21 curated questions: 11 easy, 3 medium, and 7 hard. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are sql (8), partition (7), optimization (6), spark (5), etl (4), and join (3). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
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?
Provide Pivot in PySpark example code and explain its purpose.
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