How MongoDB indexes work (B-tree mental model) - Practice Exercises

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does a B-tree index improve query speed in MongoDB?

Imagine you have a phone book sorted by last names. How does MongoDB's B-tree index help find a specific last name faster compared to scanning every entry?

A It organizes data in a tree structure allowing quick jumps to the right section instead of checking every entry.

B It stores all data in a single list and searches linearly from start to end.

C It duplicates the entire collection to speed up queries.

D It randomly picks entries until it finds the match.

💡 Hint

Think about how a dictionary helps you find words quickly by jumping to the right letter section.

A B-tree index splits data into sorted blocks, allowing MongoDB to jump directly to the block containing the searched value, avoiding scanning all entries.

❓ query_result

intermediate

What is the output count of documents matched using an indexed field?

Given a MongoDB collection users with an index on age, what will be the count result of this query?

db.users.find({ age: { $gt: 30 } }).count()

Assume the collection has 100 documents with ages ranging from 1 to 100.

A 30

B 0

C 100

D 70

💡 Hint

Count how many ages are greater than 30 in the range 1 to 100.

Ages from 31 to 100 inclusive are 70 numbers, so 70 documents match.

📝 Syntax

advanced

Which MongoDB index creation command is syntactically correct for a compound B-tree index?

Choose the correct command to create a compound index on lastName ascending and age descending fields.

A db.collection.createIndex([lastName: 1, age: -1])

B db.collection.createIndex({ lastName: 1, age: -1 })

C db.collection.createIndex({ lastName => 1, age => -1 })

D db.collection.createIndex({ lastName: 'asc', age: 'desc' })

💡 Hint

Remember MongoDB uses JSON-like objects with field names and 1 or -1 for ascending/descending.

The correct syntax uses an object with field names as keys and 1 or -1 as values to specify ascending or descending order.

❓ optimization

advanced

Which index choice optimizes queries filtering by both category and price fields?

You often query a products collection filtering by category and price. Which index will speed up these queries best?

A A text index on category and price

B Separate single-field indexes on category and price

C A compound index on { category: 1, price: 1 }

D No index, just scan the collection

💡 Hint

Think about how a single index can cover multiple fields in order.

A compound index on both fields allows MongoDB to efficiently filter by category first, then price, improving query speed compared to separate indexes.

🔧 Debug

expert

Why does this MongoDB query not use the index as expected?

Given an index on username, why does this query not use the index?

db.users.find({ username: { $regex: 'admin' } })

A Because the regex is a string pattern and MongoDB cannot use indexes with regex queries unless they are simple prefixes.

B Because regex queries with anchors (^) always ignore indexes in MongoDB.

C Because the regex is case sensitive and the index is case insensitive.

D Because regex with ^ can use the index, so this query should use it.

💡 Hint

Consider how MongoDB uses indexes with regex patterns.

MongoDB can use indexes for regex queries only if the regex is a simple prefix expression like /^prefix/. The pattern 'admin' matches anywhere in the field value and cannot use the index.