Changu Kana Thakur Arts Commerce & Science College Assistant Professor Interview Questions and Answers

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. 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.
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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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Q1. Basic details and HR round
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Q2. Technical, subject specific and Salary negotiations.
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Women Entrepreneurship development

Q1. What type of asset is land?
Ans. 
Land is a tangible, non-depreciable asset that serves as a foundational resource for various economic activities.
• Land is classified as a fixed asset in accounting, meaning it is not easily liquidated.

• It is considered a non-depreciable asset because it does not lose value over time like buildings or machinery.

• Land can be used for various purposes, such as agriculture, residential, commercial, or industrial development.

• E...read more
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Q2. Principles of accounting
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Principles of accounting are fundamental guidelines that govern financial reporting and recording.
• Accrual Principle: Revenue is recognized when earned, not when received. Example: A service performed in December is recorded in December, even if payment is received in January.

• Consistency Principle: Businesses should use the same accounting methods over time. Example: If a company uses straight-line depreciation, it shou...read more
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Q3. Cost accounting
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Q4. Debt equity ratio
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Q5. Explain book keeping
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Bookkeeping is the systematic recording and organizing of financial transactions for a business or individual.
• Involves tracking income and expenses to maintain accurate financial records.

• Utilizes methods like double-entry bookkeeping, where each transaction affects two accounts.

• Examples include recording sales, purchases, receipts, and payments.

• Essential for preparing financial statements, tax returns, and budgeting.

• Ca...read more
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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?
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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?
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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?
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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?
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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?
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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?
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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. Tell about yourself, your work experience
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Q2. Detailed subject based questions
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That was an advanced Excel test

Q1. Interaction with HR
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Q1. Interaction with the department head
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Q1. Interaction with HVC
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Interaction with HVC
• HVC stands for High Value Customer

• Discuss your experience in interacting with high value customers

• Highlight any strategies or techniques you have used to effectively engage with HVCs

• Provide examples of successful outcomes resulting from your interactions with HVCs
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