Database Management System (DBMS) Concepts Quiz

Analytical DBMS is not a type of database management system; other types include relational, NoSQL, and object-oriented DBMS.

The SQL statement 'SELECT' is used to extract data from a database; it retrieves data from one or more tables.

A primary key is a unique identifier for each row in a table, ensuring data integrity and facilitating relational database structure.

The SQL 'JOIN' clause combines rows from different tables based on a related column, facilitating data retrieval.

'OLTP' stands for Online Transaction Processing, focusing on real-time transactional processing for business applications.

'CREATE' is not a valid SQL aggregate function; common aggregate functions include COUNT, SUM, AVG, MIN, and MAX.

ACID stands for Atomicity, Consistency, Isolation, Durability, which are key properties ensuring reliable database transactions.

Data Encapsulation in DBMS involves combining data and associated methods into a cohesive unit, enhancing modularity.

NoSQL databases do not have a fixed schema; they allow flexible and dynamic schema structures.

MongoDB is an example of a Document-Oriented NoSQL database, storing data in flexible, JSON-like documents.

A 'B-tree' in database indexing is a balanced tree structure that efficiently manages sorted data for searches, access, and modifications.

Triggers in a database are special stored procedures designed to automatically execute in response to specific data modifications, enhancing automation.

In a 'Star Schema' design, a fact table holds foreign keys, measures, and facts, forming the core of a data warehouse.

'Data Mining' is the process of discovering meaningful patterns, correlations, and relationships in large datasets.

'Database Replication' involves copying and distributing data and objects between databases, ensuring consistency and availability.

'Column-oriented' DBMS stores data in columns, optimizing retrieval of large volumes of data.

'Stored Procedures' enhance security by abstracting SQL queries, reducing database access and encapsulating complex operations for efficiency.

'Locking' in database transactions prevents data corruption by ensuring exclusive access, allowing only one transaction to read or write data at a time.

'Entity-Relationship (ER) Models' provide a graphical framework for defining the logical structure of a database, representing entities, attributes, and relationships.

'Partitioning' in database systems involves dividing a database into smaller parts to enhance management, performance tuning, and backup and recovery efficiency.

'Normalization' in database design aims to reduce data redundancy and enhance data integrity by organizing data into structured and efficient forms.

Third Normal Form (3NF) in database normalization addresses transitive dependency, enhancing data integrity.

Sharding involves breaking a database into smaller, more manageable parts called shards for improved performance and scalability.

'CAP Theorem' states the inherent trade-offs in distributed databases, highlighting the challenge of simultaneously ensuring consistency, availability, and partition tolerance.

'Two-phase Commit' is a protocol ensuring synchronization in distributed transactions, requiring all participants to either commit or roll back changes.