The process of removing redundant data and ensuring data integrity in a database is known as _______.

  • Aggregation
  • Denormalization
  • Indexing
  • Normalization
The process described is known as Normalization. It involves organizing the database to minimize redundancy and dependency by dividing large tables into smaller ones and establishing relationships between them. This enhances data integrity and reduces the likelihood of anomalies.

How does generalization enhance the clarity and efficiency of a data model?

  • Increasing redundancy by duplicating attributes across entities
  • Limiting data abstraction to individual entities
  • Reducing redundancy by defining common characteristics in a superclass
  • Simplifying queries by creating complex relationships
Generalization enhances the clarity and efficiency of a data model by reducing redundancy. Common characteristics are defined in a superclass, and subclasses inherit these attributes, promoting a more organized and maintainable structure.

Scenario: A data analyst needs to query a database to extract specific information for a report. Would they likely use SQL or UML for this task, and why?

  • Both SQL and UML
  • No specific language needed
  • SQL
  • UML
A data analyst would likely use SQL (Structured Query Language) for querying a database to extract specific information for a report. SQL is specifically designed for interacting with databases, allowing the analyst to write queries to retrieve, filter, and manipulate data efficiently. UML, on the other hand, is a modeling language and is not intended for direct database querying.

What is the significance of the "column" in a column-family store?

  • It represents a data attribute
  • It represents a foreign key
  • It represents a primary key
  • It represents a record
In a column-family store, the "column" signifies a data attribute. Each column contains a specific piece of information, and rows may have varying columns based on the data they hold. This flexibility allows for dynamic and schema-less data storage, offering versatility in managing diverse datasets.

An _______ entity is one that represents a many-to-many relationship between two other entities.

  • Aggregated
  • Associative
  • Atomic
  • Derived
An associative entity is one that represents a many-to-many relationship between two other entities. It is introduced to resolve a many-to-many relationship by breaking it down into two one-to-many relationships, connecting the original entities through the associative entity.

Which type of consistency model ensures that all reads reflect the most recent write for a given data item in a distributed system?

  • Causal Consistency
  • Eventual Consistency
  • Strong Consistency
  • Weak Consistency
Strong Consistency ensures that all reads reflect the most recent write for a given data item in a distributed system. This model guarantees that any read operation will return the most recent write, providing a high level of data consistency but often at the cost of increased latency and reduced availability.

Star Schema often leads to _______ query performance compared to Snowflake Schema.

  • Better
  • Similar
  • Unpredictable
  • Worse
Star Schema often leads to Better query performance compared to Snowflake Schema. The denormalized structure of Star Schema simplifies query execution by minimizing joins, resulting in faster analytical query performance.

_______ is the process of reorganizing table and index data to improve query performance and reduce contention in a database.

  • Data Replication
  • Data Sharding
  • Database Partitioning
  • Database Tuning
Database Tuning is the process of reorganizing table and index data to enhance query performance and reduce contention in a database. It involves optimizing queries, indexing, and other database structures to achieve better efficiency.

Scenario: A financial institution needs to maintain a vast amount of transaction records while ensuring fast access to recent data. How would you implement partitioning to optimize data retrieval and storage?

  • Partitioning based on account numbers
  • Partitioning based on transaction dates
  • Partitioning based on transaction types
  • Randomized partitioning
Partitioning based on transaction dates is a recommended strategy in this scenario. It allows for segregating data based on time, making it easier to manage and retrieve recent transactions quickly. This enhances query performance and ensures that the most relevant data is readily accessible.

What are the trade-offs between strong consistency and eventual consistency in NoSQL databases?

  • Balanced latency and availability
  • High latency and low availability
  • Low latency and high availability
  • No impact on latency or availability
The trade-offs between strong consistency and eventual consistency in NoSQL databases involve choosing between low latency and high availability versus high consistency. Strong consistency ensures that all nodes see the same data at the same time, introducing higher latency and potential lower availability. On the other hand, eventual consistency prioritizes low latency and high availability, allowing nodes to have temporarily inconsistent data that will eventually converge.

Which of the following is NOT a commonly used partitioning method?

  • Hash partitioning
  • Merge partitioning
  • Range partitioning
  • Round-robin partitioning
Merge partitioning is not a commonly used partitioning method in database management. Range partitioning divides data based on specified ranges of values, hash partitioning distributes data using hash functions, and round-robin partitioning evenly distributes data across partitions without considering data characteristics.

What are some common challenges faced during conceptual schema design?

  • Ambiguous requirements
  • Indexing complexities
  • Query optimization issues
  • Schema normalization challenges
Common challenges in conceptual schema design include dealing with ambiguous requirements, where clarity is lacking. Clearing up ambiguities is crucial to ensure the final schema accurately reflects business needs.