EPAM Systems Interview Questions, Process, and Tips

Two coding questions on codility. One was easy and second medium. 10 MCQ questions on Big Data related technologies.

Q1. This round was scheduled for 1.5 hours and lasted 1 hrs 5 minutes. Discuss about projects done for previous company and architecture of the same.
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Q2. Write code for printing duplicate numbers in a list.
Ans. 
Code to print duplicate numbers in a list.
• Iterate through the list and keep track of the count of each number using a dictionary.

• Print the numbers that have a count greater than 1.
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Q3. Scala traits, higher order functions, currying
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Q4. Connecting Spark to Azure SQL Database.
Ans. 
Spark can connect to Azure SQL Database using JDBC driver.
• Download and install the JDBC driver for Azure SQL Database.

• Set up the connection string with the appropriate credentials.

• Use the JDBC API to connect Spark to Azure SQL Database.

• Example: val df = spark.read.jdbc(jdbcUrl, tableName, connectionProperties)

• Ensure that the firewall rules for the Azure SQL Database allow access from the Spark cluster.
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Q5. Elaboration of Spark optimization techniques. Types of transformations, shuffling.
Ans. 
Spark optimization techniques include partitioning, caching, and using appropriate transformations.
• Partitioning data can improve performance by reducing shuffling.

• Caching frequently used data can reduce the need for recomputation.

• Transformations like filter, map, and reduceByKey can be used to optimize data processing.

• Shuffling can be minimized by using operations like reduceByKey instead of groupByKey.

• Broadcasting sma...read more
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Q6. Difference between cache and persist, repartition and coalesce.
Ans. 
Cache and persist are used to store data in memory. Repartition and coalesce are used to change the number of partitions.
• Cache stores the data in memory for faster access while persist allows the user to choose the storage level.

• Repartition increases the number of partitions while coalesce decreases the number of partitions.

• Cache and persist are transformations while repartition and coalesce are actions.

• Cache and persi...read more
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Q7. Spark components and job execution steps.
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Q8. Hive types of tables and difference between them
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Hive has two types of tables - Managed and External. Managed tables are managed by Hive, while External tables are managed outside of Hive.
• Managed tables are created using 'CREATE TABLE' command and data is stored in Hive's warehouse directory

• External tables are created using 'CREATE EXTERNAL TABLE' command and data is stored outside of Hive's warehouse directory

• Managed tables are deleted when the table is dropped, whi...read more
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Q1. This was the final round of 1 hour and lasted 45 minutes.I was asked technical questions along with last companies project description.
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Q2. Discuss project and it's architecture.
Ans. 
Developed a data pipeline to process and analyze customer behavior data.
• Used Apache Kafka for real-time data streaming

• Implemented data processing using Apache Spark

• Stored data in Hadoop Distributed File System (HDFS)

• Used Tableau for data visualization
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Q3. Write code to print reverse of a sentence word by word.
Ans. 
Code to print reverse of a sentence word by word.
• Split the sentence into words using space as delimiter

• Store the words in an array

• Print the words in reverse order
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Q4. Difference between RDD, Dataframe, Dataset.
Ans. 
RDD, Dataframe, and Dataset are data structures in Apache Spark with different characteristics and functionalities.
• RDD (Resilient Distributed Datasets) is a fundamental data structure in Spark that represents an immutable distributed collection of objects. It provides low-level APIs for distributed data processing and fault tolerance.

• Dataframe is a distributed collection of data organized into named columns. It is simi...read more
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Q5. Lineage graph, DAG formation, RDDs characteristics
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Q1. Technical MCQ Questions on react
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Q1. Using hooks in react
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Q2. Event Loop
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Q3. Event Delegation
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Q4. Array and string related dsa questions
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Q5. Behaviour questions
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Q1. Current day to day tasks
Ans. 
My current day-to-day tasks involve designing and implementing data pipelines, optimizing data storage and retrieval, and collaborating with cross-functional teams.
• Designing and implementing data pipelines to extract, transform, and load data from various sources

• Optimizing data storage and retrieval processes for efficiency and scalability

• Collaborating with cross-functional teams to understand data requirements and del...read more
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Q2. Question on data modelling(star vs snowflake)
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Q3. Questions on data warehousing Dimensions, Facts, CDC, SCD types
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Q4. Questions on snowflake product and variety on objects there use case and optimizations, partition pruning.
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Q5. SQL queries: as per snowflake documentation 1. Read json file and extract data 2. sql query with subqueries to update records 3. sql query on window functions
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Q6. Easy Pyspark coding question.
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Q1. Project end to end detailed explanation and follow up questions.
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Q2. Questions on data warehousing concepts like facts & dimensions in detail
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Q3. Question on semi structured file formats
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Q4. Questions on snowflake product: 1. tasks 2. streams 3. time travel 4. meta data caching 5. views vs materialized views 6. how would you spin up an warehouse cluster
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Q5. Questions on spark 1. lazy evaluation 2. handling skew 3. data partitioning 4. pandas vs spark
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Q6. Questions on aws 1. lambda functions, synchronous vs asynchronous calls 2. dynamic frames, transformations 3. glue crawler 4. s3 storage classes
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Q7. SQL question using cross join, python question on string
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Q1. End to end project architecture.
Ans. 
The end-to-end project architecture involves designing and implementing the entire data pipeline from data ingestion to data visualization.
• Data ingestion: Collecting data from various sources such as databases, APIs, and files.

• Data processing: Cleaning, transforming, and aggregating the data using tools like Apache Spark or Hadoop.

• Data storage: Storing the processed data in data warehouses or data lakes like Amazon S3 ...read more
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Q2. Questions on data modelling: 1. facts and types of facts 2. dimensions and its types 3. normalization vs denormalization
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Q3. Questions on automating spark notebooks and optimization on spark job.
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Q4. How to migrate 1000s of tables using spark(databricks) notebooks
Ans. 
Use Spark (Databricks) notebooks to migrate 1000s of tables efficiently.
• Utilize Spark's parallel processing capabilities to handle large volumes of data

• Leverage Databricks notebooks for interactive data exploration and transformation

• Automate the migration process using scripts or workflows

• Optimize performance by tuning Spark configurations and cluster settings
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Q5. Questions on delta format
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Q6. Sql question on joins and numbers of records for each type of join.
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Q1. Reason for change
Ans. 
Seeking new challenges and opportunities for growth in a more dynamic environment.
• Desire for new challenges and learning opportunities

• Seeking a more dynamic work environment

• Looking to expand skill set and experience

• Interested in working on cutting-edge technologies

• Seeking better career advancement prospects
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Q2. Expectations from EPAM
Ans. 
I expect EPAM to provide challenging projects, opportunities for growth, a collaborative work environment, and support for continuous learning.
• Challenging projects that allow me to utilize my skills and knowledge

• Opportunities for professional growth and advancement within the company

• A collaborative work environment where teamwork is valued

• Support for continuous learning through training programs and resources
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Q3. Willing to relocate? when can you join
Ans. 
Yes, I am willing to relocate for the right opportunity. I can join the company within 4 weeks.
• Willing to relocate for the right opportunity

• Can join within 4 weeks

• Open to discussing relocation assistance if needed
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Q1. In-depth question related to Java garbage collections, Java multi threading, daemon thread.
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Q2. Questions related to Java streams, functional interface, and some questions implementing streams.
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Q3. Questions related to desing principles, solid, event driven, factory pattern etc.
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Q1. In-depth questions on Hibernate, Spring bean life cycles, various annotations.
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Q2. Questions related to Kafka, redis, and multithreading.
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Q3. Questions related to design pattern, behavioral design pattern, factory, observer etc.
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