Free MongoDB C100DEV Exam Questions

Question 1

You are designing a MongoDB schema for an e-commerce platform. The platform has three main collections: Products, Orders, and Users.Products Collection: Contains information about each product, such as its name, price, and description.Orders Collection: Contains details about each order, including the userId (referencing the Users collection), an array of productIds (referencing the Products collection), and the total order amount.Users Collection: Contains user details, such as name, email, and address.Which of the following data modeling approaches represents an anti-pattern in MongoDB?

A : Referencing Orders within the Users collection by storing an array of orderIds.

B : Creating a separate ProductDetails collection and storing all product details there, referencing it from the Products collection.

C : Referencing productIds in the Orders collection instead of embedding full product details.

D : Embedding user details directly in the Orders collection.

Answer: B

Question 2

Consider a MongoDB collection named orders which has documents in the following format:

{

"_id" : ObjectId("5f5b0d2f3e3dfbcc11c84444"),

"orderId" : 1001,

"customerId" : 200,

"items" : [

{

"productId" : 101,

"quantity" : 2

},

{

"productId" : 102,

"quantity" : 1

}

],

"totalAmount" : 250,

"status" : "Pending"

}

What is the correct syntax to find and update the first document in the orders collection where "status" is "Pending", and change the "status" field to "Completed" using the findAndModify method?

A : db.orders.findAndModify( { status: "Pending" }, { $set: { status: "Completed" } })

B : db.orders.findAndModify({ query: { status: "Pending" }, update: { $set: { status: "Completed" } }})

C : db.orders.findAndModify({ query: { status: "Pending" }, status: "Completed" })

D : db.orders.findAndModify({ status: "Pending" }, { status: "Completed" })

Answer: B

Question 3

We have a movies collection with the following document structure:

{

_id: ObjectId("573a1390f29313caabcd6223"),

genres: [ 'Comedy', 'Drama', 'Family' ],

title: 'The Poor Little Rich Girl',

released: ISODate("1917-03-05T00:00:00.000Z"),

year: 1917,

imdb: { rating: 6.9, votes: 884, id: 8443 }

},

{

_id: ObjectId("573a13e3f29313caabdc08a4"),

genres: [ 'Horror', 'Thriller' ],

title: 'Mary Loss of Soul',

year: 2014,

imdb: { rating: '', votes: '', id: 2904798 }

}

We need to use Aggregation Framework to calculate the following aggregates:

average imdb rating

minimum imdb rating

maximum imdb rating

Expected output:

[

{

_id: null,

avg_rating: 6.6934040649367255,

min_rating: 1.6,

max_rating: 9.6

}

]

Please note that some documents have "" (empty string) for the field "imdb.rating". Exclude these documents before aggregation.

Which pipeline should you use?

A : [{ $match: { "imdb.rating": { $ne: "" } }}, { $group: { _id: null, avg_rating: { $avg: "imdb.rating" }, min_rating: { $min: "imdb.rating" }, max_rating: { $max: "imdb.rating" } }}]

B : [{ $group: { _id: null, avg_rating: { $avg: "$imdb.rating" }, min_rating: { $min: "$imdb.rating" }, max_rating: { $max: "$imdb.rating" } }}]

C : [{ $match: { "imdb.rating": { $ne: "" } }}, { $group: { _id: null, avg_rating: { $avg: "$imdb.rating" }, min_rating: { $min: "$imdb.rating" }, max_rating: { $max: "$imdb.rating" } }}]

D : [{ $match: { "imdb.rating": { $ne: "" } }}, { $group: { _id: null, avg_rating: { avg: "$imdb.rating" }, min_rating: { min: "$imdb.rating" }, max_rating: { max: "$imdb.rating" } }}]

Answer: C

Question 4

We have an accounts collection with the following document structure:

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239d"),

account_id: 864905,

limit: 10000,

products: [ 'Commodity', 'InvestmentStock' ]

},

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239e"),

account_id: 299072,

limit: 10000,

products: [ 'InvestmentFund', 'InvestmentStock' ]

},

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239f"),

account_id: 137994,

limit: 10000,

products: [ 'CurrencyService', 'InvestmentStock' ]

}

We need to use Aggregation Framework to find the distribution of products field. Sort the result set by decreasing total number of products.

Expected output:

[

{ _id: 'InvestmentStock', total: 1746 },

{ _id: 'CurrencyService', total: 742 },

{ _id: 'Brokerage', total: 741 },

{ _id: 'InvestmentFund', total: 728 },

{ _id: 'Commodity', total: 720 },

{ _id: 'Derivatives', total: 706 }

]

Which pipeline should you use?

A : [{ $group: { _id: "$products", total: { $sum: 1 } }}]

B : [{ $unwind: { path: "$products" }}, { $group: { _id: "$products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

C : [{ $unwind: { path: "products" }}, { $group: { _id: "products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

D : [{ $group: { _id: "$products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

Answer: B

Question 5

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