NoSQL database types (document, key-value, column, graph) in DBMS Theory - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the NoSQL database type by data structure

Which NoSQL database type is best suited for storing data as flexible JSON-like documents that can vary in structure?

A Key-value store

B Graph database

C Document store

D Column store

💡 Hint

Think about a database that stores data as self-contained objects with fields and values.

Document stores save data as documents, often JSON or BSON, allowing flexible and nested data structures.

🧠 Conceptual

intermediate

Best NoSQL type for relationships between entities

Which NoSQL database type is designed to efficiently manage and query complex relationships between entities using nodes and edges?

A Column store

B Graph database

C Key-value store

D Document store

💡 Hint

Think about social networks or recommendation systems that need to explore connections.

Graph databases represent data as nodes and edges, making relationship queries fast and intuitive.

❓ query_result

advanced

Output of a key-value store query

Given a key-value store with pairs: {"user1": "Alice", "user2": "Bob", "user3": "Carol"}, what is the result of querying the key "user2"?

A "Bob"

B "user2"

C null

D Error: key not found

💡 Hint

Key-value stores return the value for the exact key queried.

Querying "user2" returns its associated value "Bob".

📋 Factual

advanced

Correct query to retrieve a column family in a column store

Which of the following is a valid CQL (Cassandra Query Language) statement to select all rows from the column family 'users'?

A SELECT * FROM users;

B FETCH ALL FROM users;

C GET * FROM users;

D READ * users;

💡 Hint

CQL syntax is similar to SQL for selecting data.

The correct syntax to select all rows is SELECT * FROM users;.

❓ optimization

expert

Optimizing queries in a graph database

Which approach best improves query performance when searching for shortest paths between nodes in a large graph database?

A Increase the number of columns in the column family storing nodes

B Store graph data as JSON documents for faster access

C Convert graph data into key-value pairs for quick lookup

D Use indexes on node properties and limit traversal depth

💡 Hint

Indexes help find starting points quickly and limiting depth reduces search space.

Indexes on node properties speed up locating nodes, and limiting traversal depth reduces computation for shortest path queries.