Tiger Analytics Machine Learning and Python Developer 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. How to decide upon Spark cluster sizing?
Ans. 
Spark cluster sizing depends on workload, data size, memory requirements, and processing speed.
• Consider the size of the data being processed

• Take into account the memory requirements of the Spark jobs

• Factor in the processing speed needed for the workload

• Scale the cluster based on the number of nodes and cores required

• Monitor performance and adjust cluster size as needed
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Q2. Question on Spark internals
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Implement a pipeline based on given conditions and data requirement

Q1. There were no questions as such. They want me join on very next day when the HR discussion was over and started itimidating me with aggressive approach even without discussing the salary part and the benef...read more
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Q1. 1. Types of cloud 2.Different Activity in ADF 3.Advance SQL 4.Basic OPPS concepts
Ans. 
Questions related to cloud types, ADF activities, advanced SQL, and basic OOPs concepts.
• Types of cloud include public, private, and hybrid

• ADF activities include data ingestion, transformation, and loading

• Advanced SQL includes window functions, subqueries, and joins

• Basic OOPs concepts include encapsulation, inheritance, and polymorphism
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Q1. 1. Secenario bases case study. 2.Performance tuning in Datebases and warehouse. 3.SQL Logics
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Basics of SQL, Python

Experience based questions, SQL and Python

Q1. Tell me about the projects you worked on
Ans. 
I have worked on projects involving building data pipelines, optimizing data storage, and developing machine learning models.
• Built data pipelines using Apache Spark and Airflow

• Optimized data storage by implementing partitioning and indexing strategies

• Developed machine learning models for predictive analytics
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Q2. Based on experience some questions related to projects.
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Q1. Spark and airflow related questions
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Q2. AWS services and it's related questions
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Q1. NLP based question
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Q1. SQL problems to solve nth highest salary
Ans. 
Use SQL query with subquery to find nth highest salary
• Use ORDER BY and LIMIT to get the nth highest salary

• Use a subquery to exclude the top n-1 salaries before selecting the nth highest salary
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Q1. GCP big query Architecture
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Q1. Components of Databricks
Ans. 
Databricks is a unified data analytics platform that includes components like Databricks Workspace, Databricks Runtime, and Databricks Delta.
• Databricks Workspace: Collaborative environment for data science and engineering teams.

• Databricks Runtime: Optimized Apache Spark cluster for data processing.

• Databricks Delta: Unified data management system for data lakes.
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Q2. How to read a json file
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To read a JSON file, use a programming language's built-in functions or libraries to parse the file and extract the data.
• Use a programming language like Python, Java, or JavaScript to read the JSON file.

• Import libraries like json in Python or json-simple in Java to parse the JSON data.

• Use functions like json.load() in Python to load the JSON file and convert it into a dictionary or object.

• Access the data in the JSON fi...read more
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Q3. Second highest salary SQL
Ans. 
To find the second highest salary in SQL, use the MAX function with a subquery or the LIMIT clause.
• Use the MAX function with a subquery to find the highest salary first, then use a WHERE clause to exclude it and find the second highest salary.

• Alternatively, use the LIMIT clause to select the second highest salary directly.

• Make sure to handle cases where there may be ties for the highest salary.
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Q4. How to configure the spark while creating the cluster
Ans. 
Spark cluster configuration involves setting up memory, cores, and other parameters for optimal performance.
• Specify the number of executors and executor memory

• Set the number of cores per executor

• Adjust the driver memory based on the application requirements

• Configure shuffle partitions for efficient data processing

• Enable dynamic allocation for better resource utilization
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