L&T Finance Data Engineer Interview Questions and Answers

Q1. Why are you choosing this job?
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Q2. What do you have qualification to get this job
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Q1. About technical skills
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Data related to finance

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Q1. Questions regarding EDA
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Q2. Questions regarding SQL
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Q3. Questions regarding Power Bi
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Q1. About case studies regarding financial data
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Asked questions of finance and aptitude

Q1. What is machine learning?
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Machine learning is a branch of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data.
• Machine learning is a subset of artificial intelligence that focuses on developing algorithms that can learn from and make predictions or decisions based on data.

• It involves training models on large datasets to recognize patterns and m...read more
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Q2. What do you know about SQL?
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SQL is a programming language used for managing and manipulating relational databases.
• SQL stands for Structured Query Language

• It is used to retrieve and manipulate data in relational databases

• Common SQL commands include SELECT, INSERT, UPDATE, DELETE

• SQL can be used to create tables, indexes, and views

• Examples of SQL databases include MySQL, PostgreSQL, Oracle
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Q3. What you know about software Development?
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Software development involves creating, designing, testing, and maintaining software applications.
• Software development includes coding, testing, debugging, and documenting software applications.

• Developers use programming languages like Java, Python, C++, etc. to write code.

• Agile and Waterfall are common software development methodologies.

• Version control systems like Git are used to manage code changes.

• Software developm...read more
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Q1. What are the algorithms you know in machine learning and their details ?
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Various machine learning algorithms with brief details
• Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest

• Unsupervised Learning: K-means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA)

• Reinforcement Learning: Q-Learning, Deep Q-Networks (DQN)

• Neural Networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), L
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Q2. Python questions
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