Real Azure data engineering questions (DP-203 aligned) — Data Factory, Synapse, ADLS Gen2, Databricks, and pipelines.
Azure powers a large share of enterprise data platforms, and Azure data engineer roles (often tied to the DP-203 certification) test the full stack. These questions cover Azure Data Factory orchestration, Synapse Analytics warehousing, Azure Databricks processing, ADLS Gen2 storage, streaming with Event Hubs/Stream Analytics, integration runtimes, security, and cost optimization. Each question includes a detailed answer.
This collection contains 50 curated questions: 25 easy, 9 medium, and 16 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 partition (16), spark (13), optimization (11), join (6), window (5), and etl (4). 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 types of triggers in ADF, including schedule, tumbling window, and event-based triggers.
Architect incremental load in ADF + Databricks with idempotency, late-arrival handling, and cost/scalability implications of watermark vs. change data capture.
Explain the difference between Azure Data Factory (ADF) and Databricks.
How do you handle data security and compliance in a cloud environment?
Triggers in ADF, especially tumbling window triggers.
What is Azure Data Factory (ADF), and what are its main components?
What is the difference between Managed and External Tables in Databricks?
What is the role of the Integration Runtime (IR) in ADF?
ADF Optimization Techniques?
Azure Fabric in Cloud Architecture?
Azure Functions vs. Logic Apps?
Business generates TBs of data daily. How would you design the data pipeline in Azure?
Can you chain multiple triggers for a single pipeline?
Can you describe the role of user groups in setting up these policies?
Compare ADF vs. Databricks.
Copy Large Files from On-Premises to Azure in ADF
Data Factory vs. Databricks: When to use which?
Data Lakehouse architecture in Azure?
Data Load in Synapse Table?
Databricks notebooks vs. Fabric notebooks - differences
Describe how to secure sensitive data in cloud storage solutions.
Describe the process and use cases of implementing Azure Data Factory pipelines.
Describe the projects emphasizing Spark, Hadoop, or Azure for large-scale data processing
Describe your experience with cloud platforms like AWS, Azure, or GCP
Difference between linked services and datasets in ADF.
Difference between pipelines and data flows in ADF
Differentiate between global and local variables in ADF.
Discuss how you integrated Azure services into your Spark application.
Discuss the tech stacks and responsibilities at Morgan Stanley
Error Handling in ADF?
Explain a linked service and how to create one.
Explain Azure Databricks architecture and its integration with other Azure services.
Explain caching techniques in Databricks.
Explain data encryption in Databricks, both at rest and in transit.
Explain GetMetadata, ForEach, and Copy Data in Azure Data Factory.
Explain how you debug failed pipelines in ADF.
Explain Microsoft Fabric and its use in data integration.
Explain Native vs. External Tables.
Explain Snowpipe as a continuous data ingestion service.
Explain steps to optimize data read performance from cloud storage (S3 or Azure Blob).
Explain the architecture of Databricks, including the control plane and data plane.
Explain the components of ADF: Pipelines, Activities, Linked Services, Datasets, Triggers, and Integration Runtimes
Explain the difference between Azure Event Hub and Azure Service Bus.
Explain the difference between Service Principal and Managed Identity in Azure.
Explain the differences between Azure IR, Self-hosted IR, and Azure-SSIS IR
Explain the differences between Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse.
Explain the key components of Apache Beam in the context of Google Dataflow.
Explain the purpose and architecture of Azure Synapse Analytics.
Explain the use of Web Activity in ADF.
Fabric dataflows vs. ADF dataflows
The core Azure data stack is Azure Data Factory (orchestration/ETL), Azure Synapse Analytics (warehousing + Spark), Azure Databricks (Spark-based processing), and ADLS Gen2 (data lake storage), plus Event Hubs/Stream Analytics for streaming and Azure SQL for relational data. The DP-203 certification and most Azure DE interviews test how these fit together into batch and streaming pipelines, plus security, partitioning, and cost.
ADF is Azure's serverless data integration and orchestration service. You build pipelines of activities that copy and transform data across 90+ connectors, using mapping data flows (Spark-backed, code-free transformations) or by invoking Databricks/Synapse. An integration runtime provides the compute. Think of ADF as the Azure equivalent of Airflow + a copy engine — it schedules, moves, and transforms data across on-prem and cloud sources.
Both process big data with Spark, but Synapse is an integrated analytics platform combining a dedicated SQL data warehouse, serverless SQL, and Spark pools with tight Power BI integration — strong for SQL-centric warehousing and BI. Databricks is a best-in-class Spark/lakehouse platform (Delta Lake, notebooks, MLflow) preferred for heavy data engineering and ML. Many shops use Databricks for transformation and Synapse (or Power BI) for serving.
Azure Data Lake Storage Gen2 is object storage built on Azure Blob with a hierarchical namespace, giving true directory semantics, fine-grained ACLs, and high-throughput analytics access. It's the storage foundation of Azure data lakes — cheap, massively scalable, and directly readable by Synapse, Databricks, and ADF. Data is typically organized in raw/curated layers using Parquet or Delta format, partitioned by date.
The integration runtime (IR) is the compute infrastructure ADF uses to run activities. Azure IR handles cloud-to-cloud data movement and data flows; Self-hosted IR runs on a machine inside your network to reach on-premises or private sources securely; Azure-SSIS IR lifts-and-shifts existing SSIS packages. Choosing the right IR is a common interview point because it determines connectivity, performance, and cost.
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