PwC Azure Cloud Engineer Interview Questions and Answers

Q1. What is polybase?
Ans. 
Polybase is a feature in Azure SQL Data Warehouse that allows users to query data stored in Hadoop or Azure Blob Storage.
• Polybase enables users to access and query external data sources without moving the data into the database.

• It provides a virtualization layer that allows SQL queries to seamlessly integrate with data stored in Hadoop or Azure Blob Storage.

• Polybase can significantly improve query performance by levera...read more
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Q2. Explain your current project architecture.
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Q1. What is Medallion Architecture
Ans. 
Medallion Architecture is a data processing architecture that involves breaking down data into smaller pieces for easier processing.
• Medallion Architecture involves breaking down data into smaller pieces for easier processing

• It allows for parallel processing of data to improve performance

• Commonly used in big data processing systems like Hadoop and Spark
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Q2. What is Spark Architecture
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Spark Architecture is a distributed computing framework that provides an efficient way to process large datasets.
• Spark Architecture consists of a driver program, cluster manager, and worker nodes.

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark supports various programming languages like Scala, Java, Python, and SQL.

• It includes components like Spark Core, Spark SQL, Spa...read more
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Q3. Find the second highest salary in employee table
Ans. 
Use SQL query to find the second highest salary in employee table
• Use SQL query with ORDER BY and LIMIT to get the second highest salary

• Example: SELECT DISTINCT salary FROM employee ORDER BY salary DESC LIMIT 1, 1
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Q1. How do you perform Partitioning
Ans. 
Partitioning in Azure Data Engineer involves dividing data into smaller chunks for better performance and manageability.
• Partitioning can be done based on a specific column or key in the dataset

• It helps in distributing data across multiple nodes for parallel processing

• Partitioning can improve query performance by reducing the amount of data that needs to be scanned

• In Azure Synapse Analytics, you can use ROUND_ROBIN or H
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Q2. What are your current responsibilities as Azure Data Engineer
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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
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Coding round will consists of SQL and pyspark questions, it's a medium level

Q1. Day to day activities
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Q2. Challenging problem
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Designing a data pipeline to process and analyze large volumes of real-time data from multiple sources.
• Identify the sources of data and their formats

• Design a scalable data ingestion process

• Implement data transformation and cleansing steps

• Utilize Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for processing and analysis
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Q1. Write dockerfile
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A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image.
• Start with a base image using the FROM keyword

• Use the RUN keyword to execute commands in the container

• Use the COPY keyword to add files from your local machine to the container

• Use the CMD keyword to specify the command to run when the container starts
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Q2. Write yaml file
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Creating a YAML file for Azure DevOps
• Use proper indentation with spaces, not tabs

• Start with '---' to indicate the beginning of the YAML file

• Define stages, jobs, and tasks within the YAML file

• Use variables and templates for reusability

• Utilize YAML anchors and aliases for DRY (Don't Repeat Yourself) code
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Q1. Incremental load
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Q2. SQL stored procedures
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Q1. What are the difference b/w data lake gen1 and gen2
Ans. 
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 acce...read more
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