General Motors General Purchase Engineer Trainee Interview Questions and Answers

Duration : 1 hour
Topics : Java and DSA

Q1. Linked List, Stack and Queue
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Q2. BFS vs DFS. Graph Traversal
Ans. 
BFS explores neighbors before moving to next level, while DFS explores as far as possible before backtracking.
• BFS uses a queue to keep track of nodes to visit next, while DFS uses a stack or recursion.

• BFS is optimal for finding shortest path in unweighted graphs, while DFS is more memory efficient.

• BFS is typically implemented iteratively, while DFS can be implemented recursively.

• Example: BFS is used in level order trav...read more
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Q1. Why do you want to join us
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Q2. Where do you see yourself in next 5 years.
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Q1. Basic Java Question
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Q2. Find out Second Highest Employee Salary
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Q1. Java 1.8 questions
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Q2. AWS related questions
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All types of aptitude, verbal, coding questions, and technical questions.

Q1. Resume related and basic computer science concepts
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Basic Arithemetic , Algebra , Geometry , Percentages , Ratios , Number Series

Linked list and tree based questions

An aptitude test is an exam used to determine an individual's skill or propensity to succeed in a given activity

Programming questions are an integral part of an interview for the developer's position

Q1. Tell me about About yourself
Ans. 
I am a passionate software developer with experience in Java, Python, and web development.
• Experienced in Java, Python, and web development

• Strong problem-solving skills

• Team player with excellent communication skills
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Q2. Describe your projects
Ans. 
I have worked on various projects including web development, mobile app development, and data analysis.
• Developed a web application for a small business to manage inventory and sales

• Created a mobile app for tracking fitness goals and progress

• Performed data analysis on customer feedback to improve user experience
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Q3. How you implement machine learning algorithms in cars
Ans. 
Machine learning algorithms in cars are implemented through sensors, data collection, training models, and real-time decision-making.
• Collecting data from various sensors installed in the car, such as cameras, lidar, radar, and GPS.

• Training machine learning models using the collected data to recognize patterns and make predictions.

• Implementing real-time decision-making algorithms in the car's onboard computer to assist ...read more
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