Equity Data Science Interview Questions and Answers

Quant test and IQ test of 1 hr

Q1. HTTP status code & it's explanation
Ans. 
HTTP status codes are standardized codes that indicate the outcome of a HTTP request.
• Status codes are grouped into 5 categories: informational, success, redirection, client error, and server error

• Some common status codes include 200 OK, 404 Not Found, and 500 Internal Server Error

• Status codes provide information about the success or failure of a request and can be used for troubleshooting and error handling
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Q2. Design API, SQL question
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Q3. Programming question
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Q1. Rude and unprofessional HR
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It was a simple aptitude online test with questions from general aptitude.

Coding test had MCQs

Q1. Questions from SQl was asked like how will we get desired output from a given table
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Q2. Questions from node js
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Q3. Questions about deployment on Linux systems
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Q1. Questions were from javascript. They will give you a problem that you have to solve in javascript.
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Logical Reasoning,Quant

Q1. Tell me about yourself
Ans. 
I am a dedicated Automation Test Engineer with 5 years of experience in creating and executing test cases for software applications.
• 5 years of experience in automation testing

• Proficient in creating and executing test cases

• Skilled in using automation tools like Selenium and Appium
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Q2. Explain project framework.
Ans. 
Project framework is a structure that provides guidelines, tools, and libraries for developing and maintaining automation test scripts.
• Project framework defines the organization of test scripts, test data, and test results.

• It includes reusable components, libraries, and utilities for common functionalities.

• Frameworks like Selenium WebDriver, TestNG, and Cucumber are commonly used in automation testing.

• Frameworks help i...read more
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Q1. Limitations of aws lambda service
Ans. 
Limitations of AWS Lambda service
• Limited execution time (15 minutes maximum)

• Limited memory allocation (up to 3GB)

• Cold start latency can impact performance

• Limited support for long-running processes

• Difficulty in debugging and monitoring
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Q2. Parallel data processing using aws services
Ans. 
Parallel data processing using AWS services involves distributing data processing tasks across multiple resources for faster and more efficient processing.
• Use AWS Glue for ETL (Extract, Transform, Load) tasks in parallel

• Leverage AWS EMR (Elastic MapReduce) for processing large amounts of data in parallel using Hadoop or Spark

• Utilize AWS Lambda for serverless parallel processing of small tasks

• Implement AWS Batch for par
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Q3. Describe your role and responsibility
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Q1. What do you know about snowflake ❄
Ans. 
Snowflake is a cloud-based data warehousing platform that allows users to store and analyze large amounts of data.
• Snowflake is a fully managed service that works on a pay-as-you-go model

• It separates storage and compute resources, allowing users to scale each independently

• Snowflake supports SQL queries and has built-in support for semi-structured data like JSON and Avro
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Q2. Window function in sql
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Window functions in SQL are used to perform calculations across a set of table rows related to the current row.
• Window functions are used to calculate values based on a set of rows related to the current row

• They can be used with aggregate functions like SUM, AVG, COUNT, etc.

• Common window functions include ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, etc.
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Q3. Python data structure
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