JLL Technologies Data Engineer Interview Questions and Answers

Q1. General Data Warehousing questions like explain your pipeline, how you implemented scd2?
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Q2. SQL questions like increment top 5th salary by 10k, last day of month, etc.
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Q1. Normal questions on spark, hadoop, sql
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Q2. One python coding question was there
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Q1. Python coding felt difficult, its a moderate difficult question
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Q2. Questions on project, sql moderate questions
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Q1. Mostly on cloud tools
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Q1. General questions about data engineering
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Q1. General question on data topics and cloud
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Q1. Salary and location
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Q1. Self Introduction
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Q2. About the current project
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Q3. Different types of joins in SQL
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Different types of joins in SQL include inner join, left join, right join, and full outer join.
• Inner join: Returns rows when there is a match in both tables.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows from the left table.

• Full outer join: Returns rows when there is a match in either table.
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Q4. Questions related to Cloud
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Q1. Roles and responsibilities in current project
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Q2. Optimizations used in present project
Ans. 
Various optimizations such as indexing, caching, and parallel processing were used in the project.
• Implemented indexing on frequently queried columns to improve query performance

• Utilized caching mechanisms to store frequently accessed data and reduce database load

• Implemented parallel processing to speed up data processing tasks

• Optimized algorithms and data structures for efficient data retrieval and manipulation
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Q3. How did you overcome out of memory issues
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I optimized code, increased memory allocation, used efficient data structures, and implemented data partitioning.
• Optimized code by identifying and fixing memory leaks

• Increased memory allocation for the application

• Used efficient data structures like arrays, hashmaps, and trees

• Implemented data partitioning to distribute data across multiple nodes
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Q1. Pyspark - How to add new column to the data How to read data from Csv file
Ans. 
To add a new column to data in Pyspark, use 'withColumn' method. To read data from a CSV file, use 'spark.read.csv' method.
• To add a new column to data in Pyspark, use 'withColumn' method

• Example: df.withColumn('new_column', df['existing_column'] * 2)

• To read data from a CSV file, use 'spark.read.csv' method

• Example: df = spark.read.csv('file.csv', header=True, inferSchema=True)
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Basic with python SQL and data models

Q1. Real life example
Ans. 
Developing a data pipeline to analyze customer behavior for an e-commerce company
• Collecting and storing customer data from website interactions

• Cleaning and transforming data to identify patterns and trends

• Building machine learning models to predict customer behavior

• Visualizing insights for stakeholders to make data-driven decisions
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Q2. Scenario based questions
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