What is the result of applying aggregation functions to a dataset in a database?

  • A summary or statistical result
  • Detailed records of individual entries
  • No change in the dataset
  • Randomized order of records
Applying aggregation functions to a dataset in a database results in a summary or statistical outcome. Instead of displaying detailed records, these functions provide valuable insights into the dataset, such as total, average, maximum, minimum, or count, helping in the analysis and interpretation of the data.

How does a composite attribute differ from a simple attribute?

  • A composite attribute can be divided into smaller, independent sub-parts
  • A composite attribute is always derived, while a simple attribute is inherent
  • A simple attribute can be divided into smaller, independent sub-parts
  • A simple attribute is composed of multiple sub-parts
A composite attribute is one that can be divided into smaller, independent sub-parts, each with its own meaning. In contrast, a simple attribute is indivisible and represents an elementary piece of data. Composite attributes provide a way to model complex information in a database.

Which of the following techniques can be employed for database performance tuning?

  • Data isolation
  • Data replication
  • Data validation
  • Denormalization
Denormalization is one of the techniques employed for database performance tuning. It involves intentionally introducing redundancy into a database schema to improve read performance by reducing the need for joins and simplifying data retrieval operations.

Scenario: A university has students and courses. Each student can enroll in multiple courses, and each course can have multiple students enrolled in it. What type of entity would you introduce to represent the relationship between students and courses in an ERD?

  • Association entity
  • Composite entity
  • Derived entity
  • Intersection entity
In this case, introducing an Intersection entity (or associative entity) is suitable. It represents the many-to-many relationship between students and courses and stores additional attributes related to the enrollment, such as enrollment date or grades.

What strategies can be employed to optimize indexing for large-scale databases?

  • Avoid indexing altogether for large-scale databases
  • Choose appropriate column(s) for indexing
  • Regularly rebuild all indexes
  • Use fewer indexes to minimize overhead
Optimizing indexing for large-scale databases involves choosing appropriate columns for indexing, considering the query patterns. It's essential to strike a balance between query performance and maintenance overhead.

What does a modality of "Optional" mean in a relationship?

  • The relationship is mandatory for all entities involved
  • The relationship is not necessary for the entities involved
  • The relationship is optional for all entities involved
  • The relationship is optional for one entity and mandatory for the other entity
In a relationship with a modality of "Optional," it means that the relationship is optional for all entities involved. This implies that an entity can exist without being associated with another entity through the specified relationship.

In database performance tuning, _______ is the process of rearranging the way data is stored to improve query performance.

  • Clustering
  • Denormalization
  • Partitioning
  • Sharding
In database performance tuning, clustering is the process of rearranging the way data is stored to improve query performance. Clustering involves storing related data together physically on the disk, which can reduce disk I/O and improve query speed.

How does collaborative data modeling differ from individual data modeling?

  • It focuses on creating data models for personal use only
  • It has no impact on the overall data modeling process
  • It involves multiple individuals working together on the same data model
  • It uses different symbols in data modeling diagrams
Collaborative data modeling involves multiple individuals working together on the same data model, fostering teamwork and incorporating diverse perspectives. This approach enhances the quality and completeness of the data model compared to individual efforts.

The purpose of _______ is to improve query performance by organizing table data based on predefined criteria.

  • Data Fragmentation
  • Database Indexing
  • Horizontal Sharding
  • Vertical Sharding
The purpose of Database Indexing is to improve query performance by organizing table data based on predefined criteria. Indexing creates a data structure that allows for faster retrieval of information, especially in large databases.

In NoSQL databases, which consistency model sacrifices consistency in favor of availability and partition tolerance?

  • Causal Consistency
  • Eventual Consistency
  • Sequential Consistency
  • Strong Consistency
Eventual Consistency in NoSQL databases sacrifices immediate consistency in favor of high availability and partition tolerance. It allows replicas of data to become consistent over time, ensuring that all replicas will eventually converge to the same value. This trade-off is suitable for systems where availability is crucial, and temporary inconsistencies can be tolerated.

Scenario: A hospital manages doctors, patients, and appointments. Each patient can have multiple appointments, each doctor can have multiple appointments, and each appointment is associated with one patient and one doctor. How would you represent this scenario in an ERD?

  • Many-to-Many
  • Many-to-One
  • One-to-Many
  • One-to-One
For this scenario, a One-to-One relationship is appropriate. Each appointment is associated with one patient and one doctor. It ensures that each appointment is uniquely linked to a specific patient and doctor, avoiding data redundancy.

In Forward Engineering, the process starts with a _______ data model and progresses towards a detailed physical model.

  • Abstract
  • Conceptual
  • Concrete
  • Logical
In Forward Engineering, the process begins with a Logical Data Model. This model represents the abstract structure of the data without concerning itself with the physical implementation. It serves as a bridge between the high-level conceptual model and the detailed physical model.