Are you preparing for a Data Engineer interview? Here are 15 real-life questions asked in interviews along with brief answers to help you revise key concepts.
🔹 Q1. Which Integration Runtime (IR) should be used for copying data from an on-premise database to Azure?
Answer: Self-hosted Integration Runtime (SHIR) is required for securely moving data between an on-premise database and Azure since it allows ADF to access private network resources.
🔹 Q2. Explain the differences between a Scheduled Trigger and a Tumbling Window Trigger in Azure Data Factory. When would you use each?
Answer:
- Scheduled Trigger runs at a fixed time, e.g., daily at 12 AM.
- Tumbling Window Trigger processes data in fixed time intervals, ensuring no overlap (e.g., hourly incremental loads).
💡 Use Scheduled Trigger for simple time-based executions and Tumbling Window Trigger for time-sliced historical data processing.
🔹 Q3. What is Azure Data Factory (ADF), and how does it enable ETL and ELT processes in a cloud environment?
Answer: ADF is a cloud-based data integration service that enables ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). It helps move and transform data using pipelines, linked services, datasets, and data flows.
🔹 Q4. Describe Azure Data Lake and its role in a data architecture. How does it differ from Azure Blob Storage?
Answer: Azure Data Lake is designed for big data analytics and supports hierarchical storage with fine-grained security, whereas Azure Blob Storage is a general-purpose object store for unstructured data.
🔹 Q5. What is an index in a database table? Discuss different types of indexes and their impact on query performance.
Answer: Indexes speed up data retrieval. Common types:
- Clustered Index: Determines data storage order.
- Non-Clustered Index: Stores a separate lookup table.
- Full-text Index: Used for text search.
📌 Indexes improve read performance but can slow down write operations.
🔹 Q6. Given two datasets, explain how the number of records will vary for each type of join (Inner Join, Left Join, Right Join, Full Outer Join).
Answer:
- Inner Join: Returns matching records only.
- Left Join: Returns all left table records + matching right table records.
- Right Join: Returns all right table records + matching left table records.
- Full Outer Join: Returns all records from both tables (matches + non-matches).
🔹 Q7. What are the Control Flow activities in Azure Data Factory? How do they differ from Data Flow activities and their typical use cases?
Answer:
- Control Flow activities manage pipeline execution (e.g., looping, conditionals).
- Data Flow activities handle data transformations (e.g., joins, aggregations).
💡 Use Control Flow for orchestration and Data Flow for data processing.
🔹 Q8. Discuss key concepts in data modeling, including normalization and denormalization. How do security concerns influence your choice of Synapse table types in a given scenario?
Answer:
- Normalization: Reduces redundancy, improves integrity.
- Denormalization: Improves read performance by duplicating data.
💡 Choose dedicated Synapse pools for high-performance queries and serverless pools for cost-effective exploratory analysis.
🔹 Q9. What are the different types of Integration Runtimes (IR) in Azure Data Factory? Discuss their use cases and limitations.
Answer:
1) Azure IR – For cloud data movement.
2) Self-hosted IR – For on-prem & private network data.
3) Azure SSIS IR – For running SSIS packages in Azure.
🔹 Q10. How can you mask sensitive data in Azure SQL Database? What are the different masking techniques available?
Answer: Use Dynamic Data Masking (DDM) with techniques like:
🔹 Default Masking
🔹 Email Masking
🔹 Random Number Masking
🔹 Custom String Masking
🔹 Q11. What is Azure Integration Runtime (IR), and how does it support data movement across different networks?
Answer: Azure IR provides secure, scalable data movement and transformation across Azure, on-prem, and hybrid networks.
🔹 Q12. Explain Slowly Changing Dimension (SCD) Type 1 in a data warehouse. How does it differ from SCD Type 2?
Answer:
- SCD Type 1: Overwrites old data.
- SCD Type 2: Maintains historical records with versioning.
🔹 Q13. SQL questions on window functions – rolling sum and lag/lead based. How do window functions differ from traditional aggregate functions?
Answer: Window functions like SUM() OVER (PARTITION BY) retain row-level granularity, unlike aggregate functions that collapse rows.
🔹 Q14. What are Linked Services in Azure Data Factory, and how do they facilitate connectivity to various data sources?
Answer: Linked Services act as connection definitions to data sources (SQL, Blob, Snowflake, etc.), enabling seamless data integration.
🔹 Q15. Describe the architecture and key components of Azure Data Factory. How do these components interact to orchestrate data workflows?
Answer:
- Pipelines: Orchestrate data movement & transformation.
- Activities: Define actions (copy, transformation, lookups).
- Datasets: Represent data structures.
- Triggers: Automate pipeline execution.
Interview Tip: Be clear, concise, and provide real-world examples when answering questions