Amazon Data Engineer Interview Questions, Process, and Tips

Oops dsa sql network

Q1. Dsa based python question
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Q2. Dsa based python question on tress
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Q1. Sql question queries
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Q2. Sql question query
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Majorly the interviews revolve around the following skills :
1. Advanced SQL
2. PYTHON (DSA)

Majorly the interviews revolve around the following skills :
1. Advanced SQL
2. python (DSA)

Q1. Which techniques you would have used on a system you have worked in the past to be able to optimise it further. (Deep knowledge of the databases is required)
Ans. 
I would have used indexing, query optimization, and data partitioning to optimize the system.
• Implement indexing on frequently queried columns to improve search performance.

• Optimize queries by using proper joins, filters, and aggregations.

• Partition large tables to distribute data across multiple storage devices for faster access.

• Use materialized views to precompute and store aggregated data for quicker retrieval.
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Q2. Situational questions on the ETL tools used and advantages and disadvantages of both
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Q1. Describe a complex situation you were in at workplace and how did you handle it?
Ans. 
Handled a complex data migration project by breaking it down into smaller tasks and collaborating with team members.
• Identified key stakeholders and their requirements

• Developed a detailed project plan with timelines and milestones

• Assigned specific tasks to team members based on their strengths and expertise

• Regularly communicated progress updates and addressed any issues promptly
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Q1. Explain data architecture of your project
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Q2. Explain data lake
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Q1. Introduce yourself
Ans. 
I am a data engineer with 5 years of experience in designing and implementing data pipelines for analytics.
• Experienced in ETL processes and data modeling

• Proficient in programming languages like Python and SQL

• Familiar with big data technologies such as Hadoop and Spark
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Q2. Talk about previous project
Ans. 
Developed a real-time data processing system for analyzing customer behavior
• Used Apache Kafka for real-time data streaming

• Implemented data pipelines using Apache Spark for processing large volumes of data

• Designed and optimized data models for efficient querying

• Integrated machine learning models for predictive analytics
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