Bajaj Finserv Devops Engineer Interview Questions and Answers

Q1. Basic devops tools questions in first round
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Q2. Scenior based question and related to you resume
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Q1. 2 coding questions on dsa
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Q1. Manager interview
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3 ques at time in coding round with mid level difficulty

Some basic aptitube and reasoning problems were asked. Easily solvable.

They asked multiple choice questions on oracle sql and pl/sql emphasizing more on reading a code block ask output questions. These were of medium hardness.

Q1. They asked what you do on daily basis in your work. What is most complex thing you have implemented and some complex questions on multilevel plsql collection module. It lasted for 1.5 hrs
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Q1. Questions regarding Oracle architecture and its component
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Q2. Question on live implementation of multilevel collection, bulk collect and record limiting and bulk exception handleing.
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Q3. Dml error logging functionality
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DML error logging functionality allows for capturing errors during data manipulation language operations.
• DML error logging can be enabled at the table level using the LOG ERRORS clause in DML statements.

• It helps in identifying and resolving data issues without causing the entire transaction to fail.

• Errors are logged in a separate error table, allowing the main operation to continue processing.

• Example: INSERT INTO table...read more
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Q4. Query and join optimization
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Q1. Related to salary and what benefits they provide.
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Q1. What years of experience do you have with cloud computing?
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I have 5 years of experience with cloud computing.
• 5 years of hands-on experience working with AWS, Azure, and Google Cloud Platform

• Implemented cloud-based solutions for scalability and cost-efficiency

• Managed cloud infrastructure, including virtual machines, storage, and networking

• Utilized cloud services like EC2, S3, RDS, and Lambda for various projects
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Q1. What’s your reason for switching jobs?
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Q1. Hadoop basics on computing
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Q2. Distribution basics for processing the data
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Q1. Architecture of bigdata systems
Ans. 
Bigdata systems architecture involves distributed storage, processing, and analysis of large volumes of data.
• Utilize distributed file systems like HDFS for storage

• Use parallel processing frameworks like Apache Spark or Hadoop for data processing

• Implement data pipelines for ETL processes

• Leverage NoSQL databases like Cassandra or MongoDB for real-time data querying

• Consider data partitioning and replication for fault tole
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Q2. How will the bigdata system distribution for storage andcompute happen
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Big data system distribution for storage and compute involves partitioning data across multiple nodes for efficient processing.
• Data is partitioned across multiple nodes to distribute storage and processing load.

• Hadoop Distributed File System (HDFS) is commonly used for storage distribution.

• Apache Spark utilizes a cluster computing framework for distributed computing.

• Data locality is important to minimize data transfer ...read more
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