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 21 curated questions: 7 easy, 5 medium, and 9 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (12), optimization (9), join (5), spark (4), window (3), and python (2). 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?
Business generates TBs of data daily. How would you design the data pipeline in Azure?
Copy Large Files from On-Premises to Azure in ADF
Data Lakehouse architecture in Azure?
Data Load in Synapse Table?
Discuss the tech stacks and responsibilities at Morgan Stanley
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 the architecture of Databricks, including the control plane and data plane.
Explain the purpose and architecture of Azure Synapse Analytics.
Fabric pipelines vs. ADF pipelines
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.
The Data Engineering Interview Answer Vault bundles 750+ reviewed answers into 7 focused PDF volumes — SQL, Spark, Python, System Design, Cloud, Behavioral, and Data Modeling. Study on any device, no subscription required.
800+ hands-on courses — Grokking System Design, Coding Patterns, and AI mock interviews for your DE loop.
Turn any topic or your own notes into an interactive, personalized course in 60 seconds.
The book that gets data engineers through system-design rounds. Essential reading.
Some links below are affiliate links. If you buy through them we may earn a small commission at no extra cost to you — it helps keep DataEngPrep free.
Reading answers is step one. Get instant AI feedback on your answers, run mock interviews, and track readiness — built specifically for data engineering interviews.