CoinDCX Senior Engineer Interview Questions and Answers

Q1. Questions related to SAP BW and HANA
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Q1. Questions related to SAP BW and HANA
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Q1. Reason for Job Change
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Q1. Latest features in iOS
Ans. 
iOS 14 introduced new features like App Library, Widgets, Picture in Picture, and Translate app.
• App Library organizes apps automatically into categories

• Widgets can be added to the home screen for quick access to information

• Picture in Picture allows for video playback while using other apps

• Translate app offers real-time translation in multiple languages
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Q2. Concepts of basic topics
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Display list of images with unit tests

Q1. Ds algo problem solving round. Question was based on graph
Ans. I had to explain the logic and code
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Q2. I was asked only 1 question and i have to write code and explain the logic
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Medium range 2 questions

Q1. Its design round question
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Ios round
questions asked involved questions from storages used in iOS
app life cycle
multiple app login with same creds autocomplete feature

Basic problem solving round
2 medium
1 b/w medium and hard
question were asked :
tree BFS and Stair question
dynamic programming

Q1. Design app like whatsapp.
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Q1. Basic firment round with director.
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Asked get write python code for a perticular scenario

Q1. Salary discussion happened
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Q1. Questions on tomcat, java., spring
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Q1. Q on OOPS, Resume based projects in detail
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Q1. Standard HR questions
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First round was coding round that comprised of 4 questions, 1 sql and 3 programming questions. Out of 3, if you are able to run 2 code successfully, you'll qualify for the next round

Q1. In this round, interviewer will ask basic interview questions first, then they'll give multiple sql questions, few questions based on spark and few programming questions
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Q1. Difference between Spark and MapReduce. Spark joins like broadcast and sort merge
Ans. 
Spark is faster than MapReduce due to in-memory processing and DAG execution.
• Spark uses DAG (Directed Acyclic Graph) execution while MapReduce uses batch processing.

• Spark performs in-memory processing while MapReduce writes to disk after each operation.

• Spark has a more flexible programming model with support for multiple languages.

• Spark has built-in libraries for machine learning, graph processing, and stream processin...read more
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Q2. Spark file formats like parquet, orc, avro etc. Which one you are using and why
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Q3. Spark optimization techniques
Ans. 
Spark optimization techniques improve performance and efficiency of Spark applications.
• Partitioning data to reduce shuffling

• Caching frequently used data

• Using broadcast variables for small data

• Tuning memory allocation and garbage collection

• Using efficient data formats like Parquet

• Avoiding unnecessary data shuffling

• Using appropriate hardware configurations

• Optimizing SQL queries with appropriate indexing and partitioning
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Q4. Basic programming language based questions
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Q5. Hive- partition vs bucketing
Ans. 
Hive partitioning is dividing data into smaller, manageable parts while bucketing is dividing data into equal parts based on a hash function.
• Partitioning is useful for filtering data based on a specific column

• Bucketing is useful for evenly distributing data for faster querying

• Partitioning can be done on multiple columns while bucketing is done on a single column

• Partitioning creates separate directories for each partiti...read more
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Q6. Hive optimization techniques
Ans. 
Hive optimization techniques improve query performance and reduce execution time.
• Partitioning tables to reduce data scanned

• Using bucketing to group data for faster querying

• Using vectorization to process data in batches

• Using indexing to speed up lookups

• Using compression to reduce storage and I/O costs
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Q7. Recent projects you've worked on
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Q8. SQL based questions, window functions
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Q1. Why do you want to switch
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Q2. Have you ever faced any problem in previous projects, how you handled that
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