ThoughtSpot Senior Software Engineer Interview Questions and Answers

Q1. Python, Django questions Easy to Medium level DSA Discussion on Project and previous work experience Some SQL queries
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Q1. Some Python questions and discussion on work experience Some design question like how you will design backend for some project from scratch Some managerial questions Questions on git
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Q1. Some basic HR questions
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Q1. Questions on java , spring boot , containarisation tools, coding, SOLID principles
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5 coading questions were asked in it

Q1. Manegerial round
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There were 4 sections-
1.Big data-some multiple choice questions
2.Python-2 basic programming questions
3.SQL-4 case questions on SQL queries
4.Pyspark-Basic multiple choice questions

Same as round 1 but there were no multiple choice questions,it was a subjective paper where they check your thought process and your approach.

Q1. Technical interview was based on -Your project discussion -Big data terminologies,Spark framework,working and architecture -Some case study questions and how you could have optimized it. -SQL basic querie...read more
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Q1. Share details of your previous job.
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Q2. Why are you looking for a change?
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Q3. What are your salary expectations?
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Q4. Tell me about yourself.
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Spark, python related questions

Q1. Previous project, scenario based problem to solve
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Q1. Package, preffered location
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Q1. What is the difference between List and Tuple?
Ans. 
List is mutable and Tuple is immutable in Python.
• List can be modified after creation while Tuple cannot be modified.

• List uses square brackets [] while Tuple uses parentheses ().

• List is used for homogenous data while Tuple is used for heterogenous data.

• List is slower than Tuple in terms of performance.

• Example of List: [1, 2, 3] and Example of Tuple: (1, 'hello', 3.14)
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Q2. What is __init__ ? Follow up question why is __ (double underscore) used?
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Q3. A SQL query based on group by and order by DESC LIMIT 1 in order to fetch highest row of a group in a dataset given by panelist.
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Q4. A simple query on SQL using group by and aggregate function.
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Q5. Panelist asked about my previous work experience and projects I worked upon earlier and the technologies used.
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Q6. Question on how do we create table using SQL Alchemy in Flask (since I had used Python Flask in one of my previous projects and had highlighted it pretty well on my resume).
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Q7. How is memory managed in Python?
Ans. 
Python uses automatic memory management through garbage collection.
• Python uses reference counting to keep track of memory usage.

• When an object's reference count drops to zero, it is deleted.

• Python also uses a garbage collector to handle circular references.

• Memory allocation is handled by the Python memory manager.

• Python provides tools like the 'gc' module for managing memory usage.
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Q1. Question on List operation.
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Q2. Do you know numpy pandas?
Ans. 
Yes, numpy and pandas are Python libraries used for data analysis and manipulation.
• NumPy is used for numerical operations on arrays and matrices.

• Pandas is used for data manipulation and analysis, providing data structures like DataFrame.

• Both libraries are commonly used in data science and machine learning.

• Example: import numpy as np; import pandas as pd;
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Q3. A question to count number of times a letter occurs in a sentence and then also find the maximum occurring letter.
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Q4. Difference between generator and iterator?
Ans. 
Generator generates values on the fly while iterator iterates over a collection of values.
• Generator is a function that returns an iterator.

• Generators use 'yield' keyword to return values one at a time.

• Iterators are objects that implement the 'next' method to return the next value in a collection.

• Iterators can be created from arrays, strings, maps, sets, etc.

• Generators are useful for generating large sequences of values...read more
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Q5. What is the selling point about you why should we consider you over other candidate?
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Q1. Why are you looking for a change?
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Q2. What are your salary expectations?
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Few MCQ's and along with couple of SQL scenario questions

Q1. More of the project experience, skillset acquired and willing to learn or not
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Q1. More of a skillset related and also the attitude
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Q1. Compensation related and also the benefits
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Q1. Spark architecture pyspark coding sql coding
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Q1. Spark architecture sql window functions,aggreate functions pyspark data frame coding
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