✅ PART 1: OBJECTIVE QUESTIONS (1 MARK EACH)
- The Turing test was invented by
Alan Turing - The full form of ER model is
Entity Relationship Model - Machine learning apps need to consider ethical guidelines.
True - Conceptual data model does not provide a technical roadmap for creating a DBMS.
True - Unsupervised machine learning requires one to provide explicit teaching signals.
False
✅ PART 2: ANSWERS
1. Purpose of Physical Data Model with respect to DBMS
The physical data model describes how data is actually stored inside the database system. It focuses on technical aspects such as file organization, indexing methods, storage structures and access paths used by the DBMS. The main purpose of the physical data model is to improve database performance and ensure efficient storage and retrieval of data. It helps database administrators decide how data will be saved on disk so that query execution becomes faster and storage space is optimized. This model is DBMS-dependent and is mainly used during the implementation phase of a database system.
2. What is Artificial Intelligence?
Artificial Intelligence is a branch of computer science that deals with the creation of intelligent machines capable of performing tasks that normally require human intelligence. These tasks include learning from experience, understanding natural language, recognizing images, solving problems and making decisions. Artificial Intelligence enables machines to simulate human thinking and behavior. Examples of artificial intelligence include voice assistants, chatbots, recommendation systems and self-driving cars. The main objective of AI is to make machines smarter and more useful in real-world applications.
3. What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence that allows machines to learn automatically from data without being explicitly programmed. In machine learning, the system analyzes past data, identifies patterns and improves its performance over time. Instead of following fixed instructions, the machine adapts itself based on experience. Machine learning is widely used in applications such as spam detection, image recognition, medical diagnosis and product recommendations. It helps systems make accurate predictions and decisions using data.
4. What do you mean by an Entity with respect to DBMS?
In DBMS, an entity refers to a real-world object or concept that can be uniquely identified and about which data is stored in a database. An entity can be physical or abstract in nature, such as a student, employee, book or course. Each entity has certain attributes that describe its properties. For example, a student entity may have attributes like roll number, name and age. Entities are represented in the database in the form of tables, where each row corresponds to one entity instance.
5. Names of popular AI software developed using Generative AI technique
Generative AI software refers to applications that can create new content such as text, images, music or code. Some popular generative AI software include ChatGPT, which generates human-like text responses, DALL·E and Midjourney, which create images from text descriptions, Google Gemini, which works as an intelligent assistant, and GitHub Copilot, which helps developers by generating code. These tools are widely used in education, design, content creation and software development.
6. What is Big Data?
Big Data refers to extremely large and complex sets of data that cannot be processed efficiently using traditional data management tools. Big Data is characterized by large volume, high speed of data generation and variety of data types such as text, images and videos. Examples of big data include social media data, online transaction records and sensor data. Big Data technologies help organizations analyze large amounts of data to gain useful insights, improve decision-making and predict future trends.
7. Advantages of using DBMS
A Database Management System provides an efficient way to store, manage and retrieve data. It reduces data redundancy by avoiding duplication and ensures data consistency and accuracy. DBMS provides better data security by controlling access to the database. It also supports backup and recovery, which helps prevent data loss. Another advantage of DBMS is that it allows multiple users to access data simultaneously in an organized manner, making data management reliable and efficient.
8. Can AI hamper the fundamental artistic creativity of humans?
Artificial Intelligence can generate creative content such as art, music and writing, but it cannot completely replace human creativity. Human creativity involves emotions, imagination and personal experiences, which AI lacks. However, excessive dependence on AI may reduce originality if humans rely too much on machines. On the other hand, AI can also support artists by providing inspiration and saving time. Therefore, AI does not destroy human creativity but should be used as a supportive tool rather than a replacement.
✅ PART 3: LONG ANSWERS (PARAGRAPH FORM, EXAM-READY)
1. Differences among Conceptual, Logical and Physical Data Models
The conceptual data model represents the high-level view of a database system and focuses on identifying entities, attributes and relationships without considering technical details. It is mainly used by end users to understand the structure of data. The logical data model provides a more detailed structure of the database by defining tables, primary keys, foreign keys and relationships, but it remains independent of any specific DBMS. The physical data model describes how data is physically stored in the database, including storage structures, indexes and access paths, and it is completely dependent on the DBMS being used.
2. Types of Attributes in ER Model
In the ER model, attributes are properties that describe an entity. Attributes can be simple, which cannot be divided further, or composite, which can be broken into sub-parts such as an address. Some attributes are single-valued, meaning they store only one value, while others are multi-valued, such as phone numbers. Derived attributes are calculated from other attributes, like age derived from date of birth. Key attributes uniquely identify an entity and play an important role in database design.
3. Definition of Supervised Machine Learning with Example
Supervised machine learning is a type of machine learning in which the model is trained using labeled data, meaning that both input and correct output are provided. The system learns by comparing its predicted output with the actual output and improving its accuracy over time. For example, in email spam detection, the machine is trained with emails labeled as spam or not spam. Based on this training, the model can correctly classify new emails.
4. Historical Significant Moments with respect to AI
The development of artificial intelligence began in 1950 when Alan Turing proposed the Turing Test to measure machine intelligence. In 1956, the term artificial intelligence was officially introduced. In 1997, IBM’s Deep Blue defeated the world chess champion, marking a major achievement in AI. The success of deep learning in 2012 further boosted AI research. In recent years, the launch of ChatGPT has made AI accessible to common users worldwide.
5. Differences between iOS and Android
iOS and Android are two popular mobile operating systems. iOS is developed by Apple and is used only in Apple devices, while Android is developed by Google and is used by many manufacturers. iOS offers better security and a controlled ecosystem, whereas Android provides more customization options. Android devices are available at various price ranges, while iOS devices are generally expensive. Both operating systems provide smooth performance and support a wide range of applications.
6. Responsibilities of a Database Administrator
A database administrator is responsible for managing and maintaining the database system. The duties include installing and configuring the database software, ensuring data security, managing user access, and performing regular backups and recovery operations. The DBA also monitors database performance and resolves issues related to data storage and access. Their role is crucial in ensuring that the database runs smoothly and securely.
7. Broad Categories of Machine Learning and their Similarities and Differences
Machine learning is broadly categorized into supervised, unsupervised and reinforcement learning. Supervised learning uses labeled data to make predictions, while unsupervised learning works with unlabeled data to discover hidden patterns. Reinforcement learning learns through rewards and penalties based on actions taken. All these categories use data and algorithms to improve performance, but they differ in their learning approach and application areas.
8. Importance of Cyber Laws in India and Government Acts
Cyber laws in India are important for protecting users from cyber crimes such as hacking, online fraud and data theft. These laws ensure safe digital transactions and protect personal information. The Information Technology Act, 2000 is the main law dealing with cyber crimes in India. The Digital Personal Data Protection Act, 2023 focuses on protecting user data. These laws help maintain trust and security in the digital environment.
9. Importance of Queries in DBMS with MySQL Queries
Queries are important in DBMS because they allow users to retrieve, insert, update and delete data from the database efficiently. Queries help in managing and analyzing data according to user requirements. In MySQL, a database can be created using the command CREATE DATABASE. A table can be added using the CREATE TABLE command, and data can be inserted into the table using the INSERT INTO command. Queries make interaction with databases simple and effective.
✅ PART 3: DESCRIPTIVE & LONG ANSWER QUESTIONS
(Very Long Answers | Simple English | Paragraph Form)
1️⃣ Differences among Conceptual Data Model, Logical Data Model and Physical Data Model
A database is designed in different stages, and each stage uses a different type of data model. The conceptual data model is the first and highest level of database design. It focuses on understanding the real-world problem and identifying important entities, their attributes and relationships. This model is created mainly for users and managers so that they can easily understand what data will be stored in the database. It does not include any technical details such as tables, keys or storage methods. The conceptual model helps in clearly representing the overall structure of the database in a simple and non-technical way.
The logical data model is the second level of database design and is more detailed than the conceptual model. It converts the conceptual model into a structured form by defining tables, columns, primary keys, foreign keys and relationships among tables. This model focuses on how data is logically organized in the database but remains independent of any specific DBMS. The logical data model ensures data consistency and reduces redundancy by properly defining relationships and constraints.
The physical data model is the final and lowest level of database design. It explains how data is physically stored in the database system. This model includes technical details such as file structure, indexing, storage format and access paths. It is completely dependent on the DBMS being used, such as MySQL or Oracle. The physical data model plays an important role in improving database performance, storage efficiency and speed of data retrieval.
2️⃣ Types of Attributes Considered in ER Model
Attributes are the properties that describe an entity in an ER model. Different types of attributes are used to represent data accurately. A simple attribute is one that cannot be divided into smaller parts, such as age or roll number. A composite attribute can be divided into multiple sub-attributes; for example, an address can be divided into street, city and state. A single-valued attribute stores only one value for an entity, while a multi-valued attribute can store multiple values, such as phone numbers or email addresses.
Derived attributes are those whose values are calculated from other attributes. For example, age can be derived from the date of birth. Key attributes are very important because they uniquely identify an entity in the database. These different types of attributes help in designing a clear and efficient database structure that accurately represents real-world data.
3️⃣ Supervised Machine Learning: Definition with Example
Supervised machine learning is a type of machine learning in which the system is trained using labeled data. In this method, both input data and correct output data are provided to the machine during the training process. The machine learns by comparing its predicted output with the actual output and then improves its accuracy over time. Supervised learning is mainly used for prediction and classification tasks.
A common example of supervised machine learning is email spam detection. In this case, the system is trained using emails that are already labeled as spam or not spam. Based on this training, the machine learns patterns in the data and can correctly classify new incoming emails. Supervised learning is widely used in applications such as medical diagnosis, face recognition and weather forecasting.
4️⃣ Historical Significant Moments in Artificial Intelligence
The history of artificial intelligence began in 1950 when Alan Turing proposed the idea of machine intelligence and introduced the Turing Test to measure whether a machine can think like a human. In 1956, the term Artificial Intelligence was officially introduced at the Dartmouth Conference, which marked the birth of AI as a scientific field. During the early years, AI focused mainly on problem-solving and symbolic reasoning.
In 1997, a major milestone was achieved when IBM’s Deep Blue defeated the world chess champion, proving that machines can outperform humans in specific tasks. In 2012, deep learning gained popularity due to its success in image recognition. In recent years, advanced AI systems like ChatGPT have transformed the way humans interact with machines by making AI more accessible and practical.
5️⃣ Differences between iOS and Android
iOS and Android are two widely used mobile operating systems, but they differ in many ways. iOS is developed by Apple and is used only in Apple devices such as iPhones and iPads. It provides a closed and highly secure ecosystem with limited customization options. Android, on the other hand, is developed by Google and is used by many manufacturers like Samsung, Xiaomi and Oppo. Android offers more customization options and flexibility to users.
In terms of cost, Android devices are available in a wide price range, while iOS devices are generally expensive. iOS is known for smooth performance and strong security, whereas Android is popular for its variety of features and open-source nature. Both operating systems support millions of applications and provide a good user experience.
6️⃣ Responsibilities of a Database Administrator (DBA)
A database administrator is responsible for the smooth functioning of a database system. The DBA installs, configures and maintains the database software. One of the most important responsibilities of a DBA is ensuring data security by controlling user access and protecting data from unauthorized use. The DBA also performs regular database backups and recovery operations to prevent data loss in case of system failure.
In addition to this, the DBA monitors database performance and optimizes queries to ensure fast data access. They also handle database updates, troubleshoot errors and ensure data integrity. The role of a DBA is essential for maintaining the reliability and efficiency of the database system.
7️⃣ Broad Categories of Machine Learning: Similarities and Differences
Machine learning is broadly divided into three categories: supervised learning, unsupervised learning and reinforcement learning. Supervised learning uses labeled data to make predictions or classifications. Unsupervised learning works with unlabeled data and is mainly used to find hidden patterns or group similar data. Reinforcement learning is based on a reward-and-penalty system, where the machine learns by interacting with its environment.
Although all three categories use data and algorithms to improve performance, they differ in their learning approach and applications. Supervised learning is commonly used in prediction tasks, unsupervised learning in data analysis, and reinforcement learning in robotics and gaming. Despite their differences, all machine learning methods aim to make systems intelligent and adaptive.
8️⃣ Importance of Cyber Laws in India and Government Acts Related to Cyber Crimes
Cyber laws are essential in India to protect individuals and organizations from cyber crimes such as hacking, online fraud, identity theft and data breaches. With the rapid growth of digital technology and online transactions, cyber laws help maintain trust and security in the digital environment. These laws also ensure the legal recognition of electronic records and digital signatures.
The Information Technology Act, 2000 is the main cyber law in India that deals with cyber crimes and electronic governance. The Digital Personal Data Protection Act, 2023 focuses on protecting personal data and privacy of users. These laws help in preventing misuse of technology and ensure safe and responsible use of digital platforms.
9️⃣ Importance of Queries in DBMS with MySQL Tasks
Queries play a very important role in DBMS because they allow users to interact with the database efficiently. Queries are used to retrieve specific data, insert new data, update existing records and delete unwanted data. They help users manage large amounts of data easily and accurately. Without queries, it would be very difficult to access and manipulate data stored in databases.
In MySQL, a relational database is created using the CREATE DATABASE command. A table is added to the database using the CREATE TABLE command, where columns and data types are defined. Data is inserted into the table using the INSERT INTO command. These queries make database operations simple, fast and effective.