Free MongoDB C100DEV Exam Questions

Become MongoDB Certified with updated C100DEV exam questions and correct answers

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Total 411 Questions | Updated On: Dec 18, 2025

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Question 1

Consider the following scenario: You have a MongoDB collection named users that stores user information. Each document in the collection has the following structure:

{

"_id": ObjectId("609fe4b5728ad805aef1a88d"),

"name": "John Doe",

"age": 30,

"email": "johndoe@example.com",

"address": {

"street": "123 Main St",

"city": "New York",

"state": "NY",

"zipcode": "10001"

}

You need to retrieve all the documents from the users collection and convert them to an array format. Which of the following MongoDB queries should you use for this task?

A : db.users.find().convertToArray()

B : db.users.find().toArray()

C : db.users.toArray()

D : db.users.aggregate({ $toArray: "users" })

Answer: B

Question 2

You are developing a blogging platform using MongoDB as the database. Each blog post document has a tags array field that holds a list of strings. You are tasked to implement a search functionality that allows users to find blog posts based on the tags. Which of the following MongoDB features would you utilize to implement this functionality efficiently?

A : Capped Collections

B : Sharding

C : Text Indexes

D : Compound Indexes

E : GridFS

Answer: C

Question 3

Suppose you're designing a MongoDB database for an online ticket booking system where millions of users can concurrently book tickets for various events. To maintain data consistency, you need to ensure that only one user can book a specific seat for an event at a time. Which MongoDB feature would you use to ensure data consistency in this high-concurrency situation?

A : Multi-document transactions

B : Change Streams

C : Capped Collections

D : Sharding

Answer: A

Question 4

Which of the following is the best practice for managing MongoDB data growth in a production environment (scale-out)?

A : Increasing the RAM size of the existing server.

B : Using replication.

C : Sharding the data across multiple servers.

D : Adding more disk space to the existing server.

Answer: C

Question 5

Consider the following documents in a MongoDB collection orders:

{

"_id": 1,

"customer_id": 101,

"item": "apple",

"price": 0.5,

"quantity": 100,

"date": ISODate("2022-01-01T00:00:00Z")

{

"_id": 2,

"customer_id": 102,

"item": "banana",

"price": 0.25,

"quantity": 50,

"date": ISODate("2022-01-02T00:00:00Z")

{

"_id": 3,

"customer_id": 101,

"item": "orange",

"price": 0.75,

"quantity": 75,

"date": ISODate("2022-01-03T00:00:00Z")

}

What is the aggregation pipeline to group the documents by "customer_id" and calculate the sum of the "quantity" field for each group, sorted in descending order by the sum of the "quantity" field?

A : db.runCommand({ aggregate: "orders", group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } }, sort: { total_quantity: -1 }})

B : db.orders.aggregate([ { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } }, { $sort: { total_quantity: 1 } }])

C : db.orders.aggregate([ { $groupBy: { customer_id: "$customer_id" }, total_quantity: { $sum: "$quantity" } }, { $sort: { total_quantity: 1 } }])

D : db.orders.aggregate([ { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } }, { $sort: { total_quantity: -1 } }])

Answer: D

Page: 1 / 83

Total 411 Questions | Updated On: Dec 18, 2025

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