A _______ query language is commonly used for graph traversal and manipulation in graph databases.
- Cypher
- Gremlin
- SPARQL
- SQL
A Cypher query language is commonly used for graph traversal and manipulation in graph databases. Cypher is specifically designed for querying graph data, making it expressive and efficient for tasks such as pattern matching and graph traversal.
A retail company wants to analyze its sales data. It has a fact table containing sales revenue, quantity sold, and discounts applied. What type of information would you expect to find in the corresponding dimension tables?
- Customer information, Product details, Time dimensions
- Employee details, Vendor information, Geographical data
- Inventory details, Market trends, Employee information
- Warehouse details, Shipping information, Payment methods
In the context of a retail company's sales data, corresponding dimension tables would likely include information about customers, products, and time. This allows for analysis based on customer preferences, product performance, and temporal trends.
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.
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.
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.
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.
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.
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.
One challenge of using compression techniques in database systems is _______.
- Decreased storage efficiency
- Improved data retrieval speed
- Increased processing overhead
- Limited data security
One challenge of using compression techniques in database systems is the increased processing overhead. Compression and decompression processes require additional computational resources, and striking a balance between storage savings and processing speed is crucial in database design.
What is a common challenge faced when using Key-Value Stores for complex data structures?
- Difficulty in representing relationships between data
- Inefficient for simple data retrieval
- Lack of consistency in data storage
- Limited support for large datasets
A common challenge when using Key-Value Stores for complex data structures is the difficulty in representing relationships between data. Unlike relational databases that excel in handling complex relationships through join operations, Key-Value Stores may face challenges in maintaining such associations directly.