Big Data Analyst

Big Data Analyst Interview Questions and Answers

Updated 27 Jun 2024

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Q1. Buy and Sell Stock - III

Given an array "prices". In "prices" the ith element is the price of the stock on the ith day. Your task is to find maximum profit at the end of the ith day. You may complete at max 2 tr...read more

Q2. Next Greater Element

For a given array/list of integers of size N, print the Next Greater Element(NGE) for every element. The Next Greater Element for an element X is the first element on the right side of X in ...read more

Big Data Analyst Interview Questions and Answers for Freshers

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Q3. Pairwise Sum of Hamming Distance

You are given an array ARR having N integers. Your task is to find the sum of Hamming Distance for each pair of the array elements.

Hamming Distance for two given integers 'A' an...read more

Q4. Basic HR Questions

Show me your id card
Relocation related questions
Which tech you are using
Some questions around projects
What's the famous food in the ongole ?
Who is your favorite hero ?

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Q5. Left View Of Binary Tree

Given a binary tree. Print the Left View of the Tree.

Example :

If the input tree is as depicted in the picture:

alt text

The Left View of the tree will be: 2 35 2

Input format :

Elements in t...read more

Q6. What is classification in ML , Details

Ans.

Classification in ML is a type of supervised learning where the goal is to predict the categorical class labels of new instances based on past observations.

Classification is a type of supervised learning in machine learning.
It involves predicting the categorical class labels of new instances based on past observations.
Common algorithms used for classification include Decision Trees, Random Forest, Support Vector Machines, and Logistic Regression.
Examples of classification tas...read more

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Q7. Explain a project ML/DL

Ans.

Developed a machine learning model to predict customer churn for a telecom company.

Collected and cleaned customer data including usage patterns and demographics
Used algorithms like logistic regression and random forest to train the model
Evaluated model performance using metrics like accuracy, precision, and recall
Implemented the model in production to provide real-time predictions for customer retention