_______ is a technique used to improve storage efficiency by dynamically allocating storage space based on data access patterns.
- Data Clustering
- Data Compression
- Data Partitioning
- Data Shuffling
Data Partitioning is a technique used to improve storage efficiency by dynamically allocating storage space based on data access patterns. It involves dividing large datasets into smaller, more manageable partitions. This helps in optimizing query performance and storage utilization.
_______ is a technique used to improve storage efficiency by dividing a large database table into smaller, more manageable parts.
- Data Encoding
- Data Indexing
- Data Normalization
- Data Partitioning
Data Partitioning is a technique where a large database table is divided into smaller, more manageable parts. This can improve storage efficiency, enhance query performance, and make data management more scalable. It's particularly useful for large datasets or in distributed database systems.
The second normal form (2NF) eliminates _______ dependencies.
- Composite
- Multivalued
- Partial
- Transitive
The second normal form (2NF) eliminates Partial dependencies. In 2NF, every non-prime attribute is fully functionally dependent on the primary key, addressing issues where only part of the primary key determines some non-prime attributes.
Which data structure is commonly used for indexing in databases?
- Linked List
- Queue
- Stack
- Tree
In databases, the most common data structure used for indexing is a tree structure, particularly a B-tree or a variant like B+ tree. These structures provide efficient searching and retrieval of data, making them suitable for indexing purposes.
What is the primary goal of denormalization in database design?
- Ensure data consistency
- Minimize storage space
- Normalize data
- Optimize for read operations
The primary goal of denormalization in database design is to optimize for read operations. It involves intentionally introducing redundancy to simplify queries and improve performance, especially for read-heavy applications.
What are the advantages of using Data Marts over Data Warehouses in certain scenarios?
- Data Marts are easier to scale horizontally
- Data Marts are more cost-effective for large-scale data
- Data Marts are suitable for specific business departments
- Data Marts provide real-time analytics
In certain scenarios, Data Marts offer advantages over Data Warehouses by focusing on specific business departments. This targeted approach allows for quicker implementations and more tailored solutions, making them efficient for specific analytical needs.
Scenario: A large e-commerce website stores millions of product records in its database. Customers frequently search for products based on categories, brands, and price ranges. How would you design indexes to optimize search performance for this scenario?
- Avoid indexing for better insert performance
- Create composite indexes on category, brand, and price columns
- Implement a full-text search index for keyword searches
- Use a single index on the product ID column
In this scenario, creating composite indexes on the frequently searched columns like category, brand, and price would optimize search performance. Composite indexes cover multiple columns and are efficient for queries involving those columns.
What does SQL stand for in the context of data modeling languages?
- Sequential Query Language
- Standard Query Language
- Structured Language
- System Query Language
SQL stands for Structured Query Language. It is a domain-specific language used in programming and designed for managing and manipulating relational databases. SQL is used to perform tasks such as querying data, updating data, and defining and modifying the structure of databases.
How does a Snowflake Schema differ from a Star Schema in terms of complexity?
- Both have the same complexity
- Complexity depends on the implementation
- Snowflake Schema is more complex
- Star Schema is more complex
A Snowflake Schema is generally considered more complex than a Star Schema. In a Snowflake Schema, Dimension Tables are normalized, leading to more relationships and potentially more joins in queries. This normalization can add complexity to the schema design and make queries more intricate compared to the denormalized structure of a Star Schema.
Scenario: In a social media platform, users can follow other users. However, a user cannot follow themselves. How would you enforce this constraint in the database?
- Check Constraint
- Foreign Key Constraint
- Primary Key Constraint
- Unique Constraint
To prevent a user from following themselves on a social media platform, you would use a Check Constraint. This constraint allows you to specify a condition, ensuring that the value in the follow relationship does not match the user's own ID.