MCQs on Data Modeling in MongoDB | MongoDB MCQs

MongoDB, a powerful NoSQL database, uses flexible schema design that provides scalability, adaptability, and ease of use. Understanding its core concepts like schema design strategies, relationships (one-to-one, one-to-many, many-to-many), and scalability best practices ensures optimal database performance. This guide dives into these topics and advanced strategies from expert MongoDB chapters.

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MCQs on Data Modeling in MongoDB

1. Schema Design Strategies

1. Which of the following is a key principle of schema design in MongoDB?
   a) Normalize all data
   b) Prioritize flexibility and performance
   c) Avoid indexing entirely
   d) Store all data in a single collection
2. In MongoDB, denormalization is often preferred because:
   a) It minimizes data redundancy
   b) It improves query performance
   c) It strictly follows relational database principles
   d) It simplifies schema validation
3. What does the “working set” refer to in schema design?
   a) The total database size
   b) Frequently accessed data
   c) All available indexes
   d) Archived data
4. Which MongoDB feature supports flexible schema design?
   a) Fixed table structures
   b) Dynamic schemas
   c) Primary key constraints
   d) Triggers
5. A schema design optimized for write-heavy workloads will:
   a) Use more indexes
   b) Reduce denormalization
   c) Minimize write operations
   d) Reduce updates by embedding data

2. Embedded vs. Referenced Documents

6. When should you embed documents instead of referencing?
   a) When data is frequently updated
   b) When data has a one-to-many relationship
   c) When data is queried together often
   d) When data sets are too large
7. Referenced documents are preferred when:
   a) Data is queried as a single unit
   b) Data changes frequently
   c) Data has low cardinality
   d) Data is hierarchical
8. Which of the following is NOT true about embedded documents?
   a) They avoid join operations
   b) They can grow indefinitely
   c) They support nested relationships
   d) They simplify reads
9. Referencing data in MongoDB is similar to:
   a) Using foreign keys in relational databases
   b) Flattening nested data structures
   c) Maintaining single collections
   d) Adding redundant data
10. What is the disadvantage of embedding too deeply?
    a) Increased write performance
    b) Reduced index size
    c) Potentially exceeding BSON document limits
    d) Increased query complexity

3. Relationships in MongoDB

11. In a one-to-one relationship, the best schema design strategy is:
    a) Always embedding
    b) Always referencing
    c) Embedding for tightly coupled data
    d) Avoiding relationships
12. A one-to-many relationship with frequently accessed child data should use:
    a) Referenced documents
    b) Embedded documents
    c) Separate collections for each child
    d) Flat structures
13. Which scenario is best suited for referencing in a many-to-many relationship?
    a) When relations are rarely updated
    b) When data needs high query performance
    c) When relations are sparse
    d) When using sharded clusters
14. Which collection design is recommended for storing a user’s list of addresses in MongoDB?
    a) Create a separate collection for addresses
    b) Embed addresses in the user document
    c) Use a single flat document for all users
    d) Use a hybrid of embedding and referencing
15. What is a common technique for handling many-to-many relationships in MongoDB?
    a) Embedding all related data
    b) Using a join collection
    c) Avoiding relationships altogether
    d) Storing data in a relational database

4. Best Practices for Scalability

16. What is a key consideration for scalable schema design in MongoDB?
    a) Minimizing data duplication
    b) Maximizing embedding
    c) Avoiding indexes
    d) Optimizing sharding
17. Why is it important to limit document size in MongoDB?
    a) To maximize BSON limits
    b) To reduce memory consumption
    c) To prevent index fragmentation
    d) To improve backup times
18. Sharding in MongoDB helps to:
    a) Combine collections into a single unit
    b) Distribute data across multiple servers
    c) Avoid schema validation
    d) Improve single-server performance
19. What is the recommended approach for write-heavy workloads?
    a) Normalize data
    b) Use sharding with hashed keys
    c) Reduce indexes
    d) Use a secondary index for writes
20. MongoDB indexes should be used:
    a) For every field
    b) Sparingly on queried fields
    c) Only on embedded documents
    d) Without considering storage

5. Expert Section (Chapters 6–10)

21. What is the maximum size of a BSON document in MongoDB?
    a) 8 MB
    b) 16 MB
    c) 32 MB
    d) 64 MB
22. The Aggregation Framework is best for:
    a) Complex queries and transformations
    b) CRUD operations
    c) Defining relationships
    d) Index management
23. What is a pipeline stage in MongoDB Aggregation?
    a) A single operation in the query plan
    b) A step in a data transformation process
    c) A method for defining indexes
    d) A schema validation tool
24. Which command is used to create a text index in MongoDB?
    a) createIndex({ field: “text” })
    b) db.createIndex({ field: 1 })
    c) db.createIndex({ field: “hashed” })
    d) addIndex({ field: “text” })
25. Which of these is NOT a benefit of the Aggregation Framework?
    a) Real-time data processing
    b) Efficient data transformation
    c) Advanced analytics
    d) Schema validation
26. Which sharding strategy minimizes query routing overhead?
    a) Hashed shard keys
    b) Range-based shard keys
    c) Random shard keys
    d) Sequential shard keys
27. In MongoDB, capped collections are:
    a) Fixed-size collections optimized for high throughput
    b) Unlimited collections for large data sets
    c) Auto-sharded collections
    d) Collections with nested documents
28. Which MongoDB operator is used for array aggregation?
    a) $concat
    b) $unwind
    c) $mergeObjects
    d) $lookup
29. Time-to-Live (TTL) indexes are used for:
    a) Controlling query performance
    b) Automatically deleting expired documents
    c) Managing transactions
    d) Implementing full-text search
30. What is the purpose of the $graphLookup operator?
    a) Text search optimization
    b) Recursive queries in hierarchical data
    c) Aggregating flat documents
    d) Managing indexes

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Answer Key

QNo	Answer (Option with Text)
1	b) Prioritize flexibility and performance
2	b) It improves query performance
3	b) Frequently accessed data
4	b) Dynamic schemas
5	d) Reduce updates by embedding data
6	c) When data is queried together often
7	b) Data changes frequently
8	b) They can grow indefinitely
9	a) Using foreign keys in relational databases
10	c) Potentially exceeding BSON document limits
11	c) Embedding for tightly coupled data
12	b) Embedded documents
13	c) When relations are sparse
14	b) Embed addresses in the user document
15	b) Using a join collection
16	d) Optimizing sharding
17	b) To reduce memory consumption
18	b) Distribute data across multiple servers
19	b) Use sharding with hashed keys
20	b) Sparingly on queried fields
21	b) 16 MB
22	a) Complex queries and transformations
23	b) A step in a data transformation process
24	a) createIndex({ field: “text” })
25	d) Schema validation
26	b) Range-based shard keys
27	a) Fixed-size collections optimized for high throughput
28	b) $unwind
29	b) Automatically deleting expired documents
30	b) Recursive queries in hierarchical data