Aidetic Software Interview Questions and Answers

Q1. Questions on Machine Learning and Deep Learning One Python coding question
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Q1. Questions on Machine Learning, Deep Learning and NLP 1 coding Question on Python
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Initially, I began with a medium-level question on LeetCode regarding the longest unique substring. Following that, I was asked about fundamental concepts in data engineering and two SQL questions: one to find the second highest employee salary and the other to determine the highest marks of students in each section.
here are all the questions
You have a dataset with employees, salaries, bonuses, and departments. How
would you calculate the total compensation for each department while
considering that bonuses are applied only to salaries above $50,000?




second highest sal in each department

select empId, dense_rank() over (partition by department order by salary decs) as rnk from employees_table where rank =2




in a streaming job processing event data, how would you handle late-arriving
data to ensure its included in calculations but doesnt delay real-time insights?"

Write PySpark logic to calculate the daily average sales amount for each product
category from a sales dataset spanning years. Consider that the df contains
'category', 'sales_amount', 'sale_date'


sales_df = sales_df.groupBy("category", sale_date).agg({"avg":"sales_amount"}).withColumnRenamed("avg(sales_amount), "Avg_sales_amount"")




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You have a dataset with employees' salaries, bonuses, and departments. How
would you calculate the total compensation for each department while
considering that bonuses are applied only to salaries above $50,000?




in a streaming job processing event data, how would you handle late-arriving
data to ensure its included in calculations but doesnt delay real-time insights?"

Write PySpark logic to calculate the daily average sales amount for each product
category from a sales dataset spanning years. Consider that the df contains 'category', 'sales_amount', 'sale_date'

Q1. No coding but a lot of technical questions on architecture mainly on data bricks , medellion architecture how you work with small files problem optimize keyword z indexing, adls2 most spark based scenario ...read more
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It about python basic question on string manipulation

Q1. Tell me about pyspark arcitecture
Ans. 
PySpark architecture is a distributed computing framework that allows for processing large datasets using Python.
• PySpark is built on top of Apache Spark, a fast and general-purpose cluster computing system.

• It utilizes a master-slave architecture with a driver program that communicates with a cluster manager to distribute tasks to worker nodes.

• PySpark supports various data sources such as HDFS, Apache Hive, and Apache H...read more
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Q2. Design lld for streaming
Ans. 
Designing a low-level design for streaming data processing system
• Use a distributed system like Apache Kafka for real-time data streaming

• Implement data processing pipelines using technologies like Apache Spark or Flink

• Ensure fault tolerance and scalability by using a microservices architecture

• Leverage cloud services like AWS Kinesis or Google Cloud Dataflow for managed streaming solutions
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Q1. What is the code to determine and print a happy number?
Ans. 
A happy number is a number which eventually reaches 1 when replaced by the sum of the square of each digit.
• Create a function to determine if a number is happy by repeatedly squaring the digits and summing them until the result is 1 or a cycle is detected.

• Use a set to keep track of seen numbers to detect cycles.

• Example: For number 19, the process would be 1^2 + 9^2 = 82, 8^2 + 2^2 = 68, 6^2 + 8^2 = 100, 1^2 + 0^2 + 0^2
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Q2. What is the transformer architecture in the context of neural networks?
Ans. 
Transformer architecture is a type of neural network architecture commonly used in natural language processing tasks.
• Utilizes self-attention mechanism to weigh the importance of different words in a sentence

• Consists of encoder and decoder layers for tasks like machine translation

• Introduced by the paper 'Attention is All You Need' by Vaswani et al.

• Popular implementations include BERT, GPT, and TransformerXL
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