Gian Jyoti Institute of Management & Technology Interview Questions and Answers

Q1. About calculus and algebra
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Q1. About achievements and experiences
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Q1. General questions
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Questions was related to Human resource management, financial accounting and etc.

Q1. General questions asked by HR.
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Q1. What is law of demand
Ans. 
Law of demand states that as the price of a good or service decreases, the quantity demanded increases, and vice versa.
• Inverse relationship between price and quantity demanded

• Consumers buy more of a good when its price decreases

• Illustrated by downward sloping demand curve

• Exceptions include Giffen goods and Veblen goods
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Q2. What CTC you have expected
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I am expecting a competitive CTC based on my qualifications and experience.
• I am looking for a salary that is in line with industry standards for Assistant Professor roles.

• I have considered factors such as my education, teaching experience, and research background when determining my expected CTC.

• I am open to negotiation based on the specific responsibilities and benefits offered by the institution.
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Q3. Last college Salary and why you left them
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Q4. They said they wouldn'tgive time leverage
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Q5. They treat their employees as Dictator
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Aptitude & General Questions

Q1. Department Technical subjects related questions
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Q2. Discussion on any Technical topic related subjects
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Q3. Discussion on other topic to check the knowledge
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Q1. Presentation or PPT on any department subject related topic
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Q1. Salary Discussion
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Q1. Can you tell me about yourself?
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Q1. What are the differences between machine learning and deep learning?
Ans. 
Machine learning is a subset of artificial intelligence that focuses on developing algorithms to make predictions based on data, while deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data.
• Machine learning is a broader concept that encompasses various techniques such as decision trees, support vector machines, and random forests.

• Deep learning specifically refers to ...read more
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Q2. What are the applications of supervised machine learning in modern businesses?
Ans. 
Supervised machine learning in modern businesses includes applications in customer segmentation, fraud detection, recommendation systems, and predictive analytics.
• Customer segmentation for targeted marketing campaigns

• Fraud detection in financial transactions

• Recommendation systems for personalized product suggestions

• Predictive analytics for forecasting sales or demand

• Sentiment analysis for understanding customer feedbac
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Q3. What is the difference between supervised learning and unsupervised learning in machine learning?
Ans. 
Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.
• Supervised learning requires a target variable to be predicted, while unsupervised learning does not.

• In supervised learning, the model learns from labeled examples provided in the training data, while in unsupervised learning, the model finds patterns and relationships in the data without guidance.

• Examples of super...read more
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Q4. What is the process by which a system can play a game of chess using reinforcement learning?
Ans. 
Reinforcement learning in chess involves training a system to make optimal moves based on rewards and penalties.
• The system starts by randomly exploring different moves and receives rewards or penalties based on the outcome.

• Over time, the system learns to make better moves by maximizing rewards and minimizing penalties.

• Examples of reinforcement learning in chess include AlphaZero and Stockfish, which use neural networks
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Q5. What is the Decision Tree Classifier, and what are its advantages?
Ans. 
Decision Tree Classifier is a machine learning algorithm that creates a tree-like model of decisions based on features.
• Uses tree-like structure of decisions to classify data

• Easy to interpret and visualize

• Can handle both numerical and categorical data

• Can handle multi-output problems

• Prone to overfitting if not pruned properly
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Q6. What is clustering in data mining?
Ans. 
Clustering in data mining is the process of grouping similar data points together based on certain criteria.
• Clustering is an unsupervised learning technique used to discover hidden patterns or structures in data.

• It helps in organizing data into meaningful groups without any prior knowledge of the groupings.

• Examples of clustering algorithms include K-means, Hierarchical clustering, and DBSCAN.

• Applications of clustering ...read more
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Q7. What tools are commonly used in Big Data?
Ans. 
Common tools used in Big Data include Hadoop, Spark, Kafka, and SQL databases.
• Hadoop: Distributed storage and processing framework for large data sets.

• Spark: In-memory data processing engine for speed and ease of use.

• Kafka: Distributed streaming platform for handling real-time data feeds.

• SQL databases: Traditional relational databases used for structured data storage and querying.
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Q1. What is best in us to join ?
Ans. 
Our commitment to excellence, innovation, and collaboration makes us the ideal choice for aspiring academics.
• Strong research opportunities: We encourage faculty to pursue groundbreaking research, exemplified by recent grants awarded to our team.

• Collaborative environment: Our department fosters teamwork, as seen in interdisciplinary projects that have led to significant advancements.

• Commitment to student success: We pri...read more
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Q2. How do you feel fit for the position.
Ans. 
I possess the necessary qualifications, experience, and passion for teaching and research in this academic role.
• Strong academic background: I hold a Ph.D. in my field, which equips me with in-depth knowledge.

• Teaching experience: I have taught undergraduate courses, receiving positive feedback from students for my engaging teaching style.

• Research contributions: I have published several papers in reputable journals, demo...read more
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Q1. Subject specific
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Q2. Salary negotiation
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