Scenario: A financial institution's database experiences frequent deadlock issues, impacting transaction throughput and system reliability. How would you mitigate these issues and ensure optimal database performance?
- Implement optimistic concurrency control, enable database encryption, increase server RAM, and use a distributed cache
- Increase server clock speed, archive old data, implement database replication, and enable query rate limiting
- Optimize SQL queries, increase database isolation level, use proper indexing, and implement deadlock detection mechanisms
- Upgrade the database software, implement horizontal scaling, use distributed transactions, and optimize network bandwidth
Deadlock issues in a financial institution's database can be mitigated by optimizing SQL queries, increasing the database isolation level, using proper indexing, and implementing deadlock detection mechanisms. These measures ensure optimal performance and reliability in transaction processing.
In a graph database, a _______ is a data entity represented by a node.
- Document
- Edge
- Relationship
- Vertex
In a graph database, a "Vertex" is a data entity represented by a node. A vertex typically contains properties that describe the entity, and the relationships between vertices define the connections in the graph.
Scenario: A retail store maintains a database of products and categories. Each product can belong to multiple categories, and each category can have multiple products. What type of relationship would you establish between products and categories?
- Many-to-Many
- Many-to-One
- One-to-Many
- One-to-One
The relationship between products and categories in this scenario is Many-to-Many. Each product can belong to multiple categories, and each category can have multiple products. This type of relationship is common in scenarios where entities are interconnected in a non-exclusive manner.
What is indexing in the context of relational databases?
- A data structure that enhances sorting and searching operations
- A mechanism to enforce data integrity
- A technique to encrypt sensitive data
- An approach to optimize database backup processes
Indexing in relational databases refers to the creation of a data structure that enhances sorting and searching operations. It accelerates query retrieval by providing a quick lookup mechanism, especially for large datasets, and is crucial for optimizing database performance.
What is data partitioning in the context of storage optimization?
- Backing up data regularly
- Dividing data into smaller subsets based on certain criteria
- Encrypting data for security purposes
- Merging multiple datasets into a single database
Data partitioning involves dividing large datasets into smaller, more manageable subsets based on specific criteria such as date ranges, geographic regions, or other relevant factors. This helps in optimizing storage by distributing data across different storage devices or servers efficiently.
What are derived attributes, and why are they used in database design?
- Attributes that are mandatory
- Attributes that are not essential
- Attributes that can be calculated or derived from other attributes
- Attributes with no relation to other attributes
Derived attributes in database design are those that can be calculated or derived from other attributes in the database. They are used to avoid data redundancy and improve data accuracy by ensuring that certain values are always up-to-date based on the values of other attributes.
How does partitioning contribute to storage optimization in distributed databases?
- Centralizing data storage
- Distributing data across multiple nodes
- Implementing stronger encryption
- Increasing data redundancy
Partitioning in distributed databases involves distributing data across multiple nodes. This contributes to storage optimization by allowing parallel processing, reducing load on individual nodes, and improving overall system performance. It facilitates efficient data management in large-scale distributed environments.
Scenario: A financial institution requires a data warehouse to analyze customer transactions and account balances over time. How would you utilize Dimensional Modeling principles to design the data model?
- Fact table for customers, dimensions for transactions and time
- Fact table for time, dimensions for customers and transactions
- Fact table for transactions, dimensions for customers and time
- No need for Dimensional Modeling in financial analysis
For a financial institution analyzing customer transactions and account balances, a Fact table for transactions with dimensions for customers and time is suitable. This allows for detailed analysis based on customer transactions over time.
An entity with a modality of _______ indicates that its presence is mandatory in a relationship.
- Compulsory
- Conditional
- Mandatory
- Optional
An entity with a modality of Mandatory indicates that its presence is mandatory in a relationship. This means that every instance of the entity must participate in the relationship.
How does denormalization differ from normalization in terms of database design?
- Denormalization and normalization are synonymous terms used to describe the same process in database design.
- Denormalization and normalization have no impact on query performance.
- Denormalization involves intentionally introducing redundancy into a database by combining tables to improve query performance. Normalization, on the other hand, focuses on minimizing redundancy by organizing data into separate tables and ensuring dependencies are logical.
- Denormalization is only applicable in NoSQL databases, while normalization is reserved for SQL databases.
Denormalization and normalization represent opposing strategies in database design. Denormalization intentionally introduces redundancy to enhance query performance, while normalization seeks to minimize redundancy for logical organization.