100+ Azure Data Engineer Interview Questions and Answers

Choose a cluster based on data size, complexity, and processing requirements.

• Consider the size and complexity of the data to be processed.

• Determine the processing requirements, such as batch or real-time processing.

• Choose a cluster with appropriate resources, such as CPU, memory, and storage.

• Examples of Azure clusters include HDInsight, Databricks, and Synapse Analytics.

To create mount points in ADLS, use the Azure Storage Explorer or Azure Portal. To load data source, use Azure Data Factory or Azure Databricks.

• Mount points can be created using Azure Storage Explorer or Azure Portal

• To load data source, use Azure Data Factory or Azure Databricks

• Mount points allow you to access data in ADLS as if it were a local file system

• Data can be loaded into ADLS using various tools such as Azure Data Factory, Azure Databricks, or Azure HDInsight

We should use the Self-hosted Integration Runtime (IR) to copy data from on-premise db to Azure.

• Self-hosted IR allows data movement between on-premise and Azure

• It is installed on a local machine or virtual machine in the on-premise network

• Self-hosted IR securely connects to the on-premise data source and transfers data to Azure

• It supports various data sources like SQL Server, Oracle, MySQL, etc.

• Self-hosted IR can be managed and monitored through Azure Data Factory

SQL inner and left join can be used to combine tables with duplicate values based on specified conditions.

• Use INNER JOIN to return rows from both tables that have matching values

• Use LEFT JOIN to return all rows from the left table and the matched rows from the right table

• Handle duplicate values by using DISTINCT or GROUP BY clauses

Distributed table in Synapse is a table that is distributed across multiple nodes for parallel processing.

• Distributed tables in Synapse are divided into distributions to optimize query performance.

• There are three distribution types: Hash distribution, Round-robin distribution, and Replicate distribution.

• Hash distribution is ideal for joining large tables on a common key, Round-robin distribution evenly distributes data, and Replicate distribution duplicates data on all nodes....read more

I have applied optimization techniques like partitioning, caching, and cluster sizing in Databricks projects.

• Utilized partitioning to improve query performance by limiting the amount of data scanned

• Implemented caching to store frequently accessed data in memory for faster retrieval

• Adjusted cluster sizing based on workload requirements to optimize cost and performance

ADLS is Azure Data Lake Storage, a scalable and secure data lake solution. ADLS gen2 is an improved version of gen1.

• ADLS is a cloud-based storage solution for big data analytics workloads

• ADLS gen1 is based on Hadoop Distributed File System (HDFS) and has limitations in terms of scalability and performance

• ADLS gen2 is built on Azure Blob Storage and offers improved performance, scalability, and security features

• ADLS gen2 supports hierarchical namespace, which enables efficient...read more

Accumulators are variables used for aggregating data in Spark. GroupByKey and ReduceByKey are operations used for data transformation.

• Accumulators are used to accumulate values across multiple tasks in a distributed environment.

• GroupByKey is used to group data based on a key and create a pair of key-value pairs.

• ReduceByKey is used to aggregate data based on a key and reduce the data to a single value.

• GroupByKey is less efficient than ReduceByKey as it shuffles all the data ac...read more

CTE is used to create temporary result sets, stored procedures are reusable blocks of code, left outer join combines rows from two tables based on a related column

• CTE (Common Table Expression) is used to create temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement.

• Stored Procedures are reusable blocks of code that can be executed with a single call. They can accept input parameters and return output parameters.

• Left Outer Join combin...read more

Optimization techniques in Spark improve performance and efficiency of data processing.

• Partitioning data to distribute workload evenly

• Caching frequently accessed data in memory

• Using broadcast variables for small lookup tables

• Avoiding shuffling operations whenever possible

• Tuning configuration settings like memory allocation and parallelism

Serialization is the process of converting an object into a stream of bytes for storage or transmission.

• Serialization is used to transfer objects between different applications or systems.

• It allows objects to be stored in a file or database.

• Serialization can be used for caching and improving performance.

• Examples of serialization formats include JSON, XML, and binary formats like Protocol Buffers and Apache Avro.

Spark architecture is a distributed computing framework that processes large datasets in parallel across a cluster of nodes.

• Spark has a master-slave architecture with a driver program that communicates with the cluster manager to allocate resources and tasks to worker nodes.

• Worker nodes execute tasks in parallel and store data in memory or disk.

• Spark supports various data sources and APIs for batch processing, streaming, machine learning, and graph processing.

• Azure Databricks...read more

Data bricks is a unified analytics platform for big data and machine learning, while ADF (Azure Data Factory) is a cloud-based data integration service.

• Data bricks is a unified analytics platform that provides a collaborative environment for big data and machine learning projects.

• ADF is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines.

• Data bricks supports multiple programming languages like Python, Scala, and SQL, while ADF...read more

Delta load in ADF is achieved by comparing source and target data and only loading the changed data.

• Use a Lookup activity to retrieve the latest watermark or timestamp from the target table

• Use a Source activity to extract data from the source system based on the watermark or timestamp

• Use a Join activity to compare the source and target data and identify the changed records

• Use a Sink activity to load only the changed records into the target table

IF Else activity can be implemented using the Switch activity in Azure Data Factory.

• Create a Switch activity in your pipeline

• Define the condition in the expression field

• Add cases for each condition with corresponding activities

• Add a default activity for cases that do not match any condition

Semantic layer is a virtual layer that provides a simplified view of complex data.

• It acts as a bridge between the physical data and the end-user.

• It provides a common business language for users to access data.

• It simplifies data access by hiding the complexity of the underlying data sources.

• Examples include OLAP cubes, data marts, and virtual tables.

Yes, I have worked on real-time data processing projects using technologies like Apache Kafka and Spark Streaming.

• Implemented real-time data pipelines using Apache Kafka for streaming data ingestion

• Utilized Spark Streaming for processing and analyzing real-time data

• Worked on monitoring and optimizing the performance of real-time data processing systems

You can load data from Databricks to Synapse using PolyBase or Azure Data Factory.

• Use PolyBase to load data from Databricks to Synapse by creating an external table in Synapse pointing to the Databricks data location.

• Alternatively, use Azure Data Factory to copy data from Databricks to Synapse by creating a pipeline with Databricks as source and Synapse as destination.

• Ensure proper permissions and connectivity between Databricks and Synapse for data transfer.

ADLS gen 2 is an upgrade to gen 1 with improved performance, scalability, and security features.

• ADLS gen 2 is built on top of Azure Blob Storage, while gen 1 is a standalone service.

• ADLS gen 2 supports hierarchical namespace, which allows for better organization and management of data.

• ADLS gen 2 has better performance for large-scale analytics workloads, with faster read and write speeds.

• ADLS gen 2 has improved security features, including encryption at rest and in transit.

• AD...read more

As an Azure Data Engineer, my current responsibilities include designing and implementing data solutions on Azure, optimizing data storage and processing, and ensuring data security and compliance.

• Designing and implementing data solutions on Azure

• Optimizing data storage and processing for performance and cost efficiency

• Ensuring data security and compliance with regulations

• Collaborating with data scientists and analysts to support their data needs

Data Lake Gen1 is based on Hadoop Distributed File System (HDFS) while Gen2 is built on Azure Blob Storage.

• Data Lake Gen1 uses HDFS for storing data while Gen2 uses Azure Blob Storage.

• Gen1 has a hierarchical file system while Gen2 has a flat file system.

• Gen2 provides better performance, scalability, and security compared to Gen1.

• Gen2 supports Azure Data Lake Storage features like tiering, lifecycle management, and access control lists (ACLs).

• Gen2 allows direct access to data ...read more

Window functions are used to perform calculations across a set of rows in a table.

• Window functions are used to calculate values based on a subset of rows within a table

• They are used to perform calculations such as running totals, ranking, and moving averages

• Examples of window functions include ROW_NUMBER(), RANK(), and SUM() OVER()

Yes, I have worked on developing a Data Validation Framework to ensure data accuracy and consistency.

• Developed automated data validation scripts to check for data accuracy and consistency

• Implemented data quality checks to identify and resolve data issues

• Utilized tools like SQL queries, Python scripts, and Azure Data Factory for data validation

• Worked closely with data stakeholders to define validation rules and requirements

Set up ETL flow for data in Lake House using Databricks

• Connect Databricks to Lake House storage (e.g. Azure Data Lake Storage)

• Define ETL process using Databricks notebooks or jobs

• Extract data from Lake House, transform as needed, and load into target destination

• Monitor and schedule ETL jobs for automated data processing

SQL query to fetch Top 3 revenue generating Products from Sales table

• Use the SELECT statement to retrieve data from the Sales table

• Use the GROUP BY clause to group the data by Product

• Use the ORDER BY clause to sort the revenue in descending order

• Use the LIMIT clause to fetch only the top 3 revenue generating Products

Use Azure Data Factory to copy multiple tables from on-premises to Azure Blob Storage

• Create a linked service to connect to the on-premises data source

• Create datasets for each table to be copied

• Create a pipeline with copy activities for each table

• Use Azure Blob Storage as the sink for the copied tables

Encountered a data corruption issue in Azure Data Lake Storage and resolved it by restoring from a backup.

• Identified the corrupted files by analyzing error logs and data inconsistencies

• Restored the affected data from the latest backup available

• Implemented preventive measures such as regular data integrity checks and backups

• Collaborated with the Azure support team to investigate the root cause