Persistent Systems Interview Questions, Process, and Tips

Q1. Core Java questions and multithreading basics like executor service
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Q2. Spring boot error handling, simple questions on distributed systems
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Q3. Coding question. Requested to do in O(n ) time complexity as oppsed to O(n2) suggested by me.
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Q1. Given an array find all the numbers whose sum is 11 using streams only
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Q2. Spring boot basics, hibernate queries, what is native query
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Q1. Design patterns in Java, write a singleton class
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Q2. Spring IOC and AOP
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Q1. How do you handle difficult stakeholders?
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Q2. Insurance policy lifecycle
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Q3. Simple Join sql query
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Q1. Difference between BA and PO
Ans. 
Business Analyst (BA) focuses on understanding business needs and requirements, while Product Owner (PO) focuses on defining and prioritizing product features.
• BA analyzes business processes and systems to identify areas for improvement, while PO works closely with stakeholders to define product features and prioritize the product backlog.

• BA typically works on multiple projects simultaneously, while PO is dedicated to a...read more
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Q2. No relevant questions in 2nd round
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Q1. Write a program using Java to find the counts of specified characters and replace them with corresponding numbers.
Ans. 
A Java program to find and replace specified characters with corresponding numbers in an array of strings.
• Iterate through each string in the array

• Count the occurrences of specified characters

• Replace the characters with corresponding numbers

• Return the modified array of strings
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Q2. Given an array, how can you separate the even and odd numbers, placing the even numbers on the right side and the odd numbers on the left side?
Ans. 
Separate even and odd numbers in an array, placing even numbers on the right side and odd numbers on the left side.
• Iterate through the array and check if each element is even or odd.

• Create two separate arrays to store even and odd numbers.

• Append even numbers to one array and odd numbers to another.

• Finally, combine the two arrays with even numbers on the right and odd numbers on the left.
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Q1. Tell me about yourself and Project
Ans. 
I am a Senior Data Engineer with experience in developing data pipelines and optimizing data storage for various projects.
• Developed data pipelines using Apache Spark for real-time data processing

• Optimized data storage using technologies like Hadoop and AWS S3

• Worked on a project to analyze customer behavior and improve marketing strategies
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Q2. What was you day-to-day job in your project
Ans. 
My day-to-day job in the project involved designing and implementing data pipelines, optimizing data workflows, and collaborating with cross-functional teams.
• Designing and implementing data pipelines to extract, transform, and load data from various sources

• Optimizing data workflows to improve efficiency and performance

• Collaborating with cross-functional teams including data scientists, analysts, and business stakeholde...read more
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Q3. Spark Architecture
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Q4. How DAG handle Fault tolerance?
Ans. 
DAGs handle fault tolerance by rerunning failed tasks and maintaining task dependencies.
• DAGs rerun failed tasks automatically to ensure completion.

• DAGs maintain task dependencies to ensure proper sequencing.

• DAGs can be configured to retry failed tasks a certain number of times before marking them as failed.
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Q5. What is shuffling? How to Handle Shuffling?
Ans. 
Shuffling is the process of redistributing data across partitions in a distributed computing environment.
• Shuffling is necessary when data needs to be grouped or aggregated across different partitions.

• It can be handled efficiently by minimizing the amount of data being shuffled and optimizing the partitioning strategy.

• Techniques like partitioning, combiners, and reducers can help reduce the amount of shuffling in MapRed
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Q6. What is the difference between repartition and Coelsce?
Ans. 
Repartition increases or decreases the number of partitions in a DataFrame, while Coalesce only decreases the number of partitions.
• Repartition can increase or decrease the number of partitions in a DataFrame, leading to a shuffle of data across the cluster.

• Coalesce only decreases the number of partitions in a DataFrame without performing a full shuffle, making it more efficient than repartition.

• Repartition is typically...read more
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Q7. How do you handle Incremental data?
Ans. 
Incremental data is handled by identifying new data since the last update and merging it with existing data.
• Identify new data since last update

• Merge new data with existing data

• Update data warehouse or database with incremental changes
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Q8. What is SCD ??
Ans. 
SCD stands for Slowly Changing Dimension, a concept in data warehousing to track changes in data over time.
• SCD is used to maintain historical data in a data warehouse.

• There are three types of SCD - Type 1, Type 2, and Type 3.

• Type 1 SCD overwrites old data with new data.

• Type 2 SCD creates a new record for each change, preserving history.

• Type 3 SCD maintains both old and new values in the same record.

• SCD is important for...read more
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Q9. Scenerio based questions related to Spark ?
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Q10. Two SQL Codes and Two Python codes like reverse a string ?
Ans. 
Reverse a string using SQL and Python codes.
• In SQL, use the REVERSE function to reverse a string.

• In Python, use slicing with a step of -1 to reverse a string.
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Q11. Find top 5 countries with highest population in Spark and SQL
Ans. 
Use Spark and SQL to find the top 5 countries with the highest population.
• Use Spark to load the data and perform data processing.

• Use SQL queries to group by country and sum the population.

• Order the results in descending order and limit to top 5.

• Example: SELECT country, SUM(population) AS total_population FROM table_name GROUP BY country ORDER BY total_population DESC LIMIT 5
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Q12. Using two tables find the different records for different joins
Ans. 
To find different records for different joins using two tables
• Use the SQL query to perform different joins like INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• Identify the key columns in both tables to join on

• Select the columns from both tables and use WHERE clause to filter out the different records
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Q1. What is a catalyst optimiser? How it works?
Ans. 
A catalyst optimizer is a query optimization tool used in Apache Spark to improve performance by generating an optimal query plan.
• Catalyst optimizer is a rule-based query optimization framework in Apache Spark.

• It leverages rules to transform the logical query plan into a more optimized physical plan.

• The optimizer applies various optimization techniques like predicate pushdown, constant folding, and join reordering.

• By o...read more
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Q2. Tell me about the optimization you used in your project.
Ans. 
Used query optimization techniques to improve performance in database queries.
• Utilized indexing to speed up search queries.

• Implemented query caching to reduce redundant database calls.

• Optimized SQL queries by restructuring joins and subqueries.

• Utilized database partitioning to improve query performance.

• Used query profiling tools to identify and optimize slow queries.
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Q3. Pyspark question related to merging two schemas?
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Q4. What is the best approach to finding whether the data frame is empty or not?
Ans. 
Use the len() function to check the length of the data frame.
• Use len() function to get the number of rows in the data frame.

• If the length is 0, then the data frame is empty.

• Example: if len(df) == 0: print('Data frame is empty')
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Q5. Spark Architecture
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Q6. How do you decide on cores and worker nodes?
Ans. 
Cores and worker nodes are decided based on the workload requirements and scalability needs of the data processing system.
• Consider the size and complexity of the data being processed

• Evaluate the processing speed and memory requirements of the tasks

• Take into account the parallelism and concurrency needed for efficient data processing

• Monitor the system performance and adjust cores and worker nodes as needed
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Q7. What happens when we enforce schema ?
Ans. 
Enforcing schema ensures that data conforms to a predefined structure and rules.
• Ensures data integrity by validating incoming data against predefined schema

• Helps in maintaining consistency and accuracy of data

• Prevents data corruption and errors in data processing

• Can lead to rejection of data that does not adhere to the schema
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Q1. Genai, mL, roadmap, plan
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Project roadmap and risk assessment

Q1. Asked about GitHub Actions and octopus Deploy and AWS related questions
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Q2. Terraform about daily tasks
Ans. 
Terraform daily tasks involve infrastructure provisioning, configuration management, and automation.
• Creating and managing infrastructure using Terraform scripts

• Updating and modifying existing infrastructure as needed

• Automating deployment processes for applications

• Implementing version control for Terraform configurations

• Monitoring and troubleshooting Terraform deployments
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Q1. Explain about RPA,
Ans. 
RPA stands for Robotic Process Automation, which involves using software robots to automate repetitive tasks.
• RPA uses software robots to automate repetitive tasks, mimicking human actions.

• It can be used to streamline business processes, increase efficiency, and reduce human error.

• RPA tools can interact with existing applications, extract data, and perform tasks across multiple systems.

• Examples of RPA tools include UiPa
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Q2. What RE-Framework
Ans. 
RE-Framework is a Robotic Enterprise Framework for automating business processes using UiPath.
• RE-Framework is a template designed by UiPath for building scalable and efficient automation projects.

• It includes pre-built workflows for exception handling, logging, and reusability of components.

• RE-Framework follows a state machine design pattern to manage the flow of automation.

• It allows for easy integration of new processe
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Q3. Etc..
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Q1. One on One Round of Interview with Lient
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Q1. Salary Discussion
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