Nineleaps Technology Solutions Senior Data Engineer Interview Questions and Answers

Basic Questions on python related to strings

Q1. What method to reduce time complexity
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Q2. Which technology will suit a particylar situation
Ans. 
Choosing the right technology depends on the specific requirements of the situation.
• Consider the data size and complexity

• Evaluate the processing speed and scalability

• Assess the cost and availability of the technology

• Take into account the skillset of the team

• Examples: Hadoop for big data, Spark for real-time processing, AWS for cloud-based solutions
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This was my first campus drive,& it was one of the hardest one too.We had 3 coding problems for 60 mins.It was held on HackerRank & it was not so hard but not so easy for the beginners.
1)Lambda Sort
2) and 3) ques on SQL
It was different ques for each one.

Q1. The interviewer asked all the concepts of pyspark, ETL and a code on dsa
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It was a coding round with sql ,python questions , few aptitude questions

Q1. They asked few questions sql and python and few questions - depending on the role
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Q2. BASIC SQL WHAT YOU HAD WRITTEN IN THE CODE
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Q1. They asked me few questions related to sql
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Q2. BASIC PYTHON QUESTIONS LIKE WHAT YOU HAVE WRITTEN IN THE CODE
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Q1. About yourself , your hobby etc
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Python and SQL questions were asked

Q1. I was asked about the project
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Q1. ACID properties of DBMS
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ACID properties ensure data integrity in DBMS: Atomicity, Consistency, Isolation, Durability.
• Atomicity ensures that all operations in a transaction are completed successfully or none at all.

• Consistency ensures that the database remains in a consistent state before and after the transaction.

• Isolation ensures that multiple transactions can be executed concurrently without affecting each other.

• Durability ensures that once...read more
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Q1. HR question and family background
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Q1. Tell me about your current project? Have you used any AWS services?
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Q2. Can you explain about EMR andEC2 instance?
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EMR is a managed Hadoop framework for processing large amounts of data, while EC2 is a scalable virtual server in AWS.
• EMR stands for Elastic MapReduce and is a managed Hadoop framework for processing large amounts of data.

• EC2 stands for Elastic Compute Cloud and is a scalable virtual server in Amazon Web Services (AWS).

• EMR allows for easy provisioning and scaling of Hadoop clusters, while EC2 provides resizable compute...read more
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Q3. What type of schemas did you use for your project. (Star schema, Snowflake Schema)
Ans. 
I have experience working with both Star and Snowflake schemas in my projects.
• Star schema is a denormalized schema where one central fact table is connected to multiple dimension tables.

• Snowflake schema is a normalized schema where dimension tables are further normalized into sub-dimension tables.

• Used Star schema for simpler, smaller datasets where performance is a priority.

• Used Snowflake schema for complex, larger dat...read more
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Q4. Have you used python, pyspark in your projects?
Ans. 
Yes, I have used Python and PySpark in my projects for data engineering tasks.
• I have used Python for data manipulation, analysis, and visualization.

• I have used PySpark for big data processing and distributed computing.

• I have experience in writing PySpark jobs to process large datasets efficiently.
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Q5. Do you have any experience with serverless schema?
Ans. 
Yes, I have experience with serverless schema.
• I have worked with AWS Lambda to build serverless applications.

• I have experience using serverless frameworks like Serverless Framework or AWS SAM.

• I have designed and implemented serverless architectures using services like AWS API Gateway and AWS DynamoDB.
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