Free MongoDB C100DEV Exam Questions

Question 1

Consider a collection named orders with the following documents:

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e1"),

"order_id" : 101,

"customer_name" : "John Doe",

"order_date" : ISODate("2022-01-01T00:00:00.000Z"),

"items" : [

{

"product_name" : "Apple",

"quantity" : 2,

"price" : 0.99

},

{

"product_name" : "Banana",

"quantity" : 5,

"price" : 0.5

}

]

}

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e2"),

"order_id" : 102,

"customer_name" : "Jane Doe",

"order_date" : ISODate("2022-01-02T00:00:00.000Z"),

"items" : [

{

"product_name" : "Orange",

"quantity" : 3,

"price" : 0.79

},

{

"product_name" : "Mango",

"quantity" : 2,

"price" : 1.99

}

]

}

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e3"),

"order_id" : 103,

"customer_name" : "Bob Smith",

"order_date" : ISODate("2022-01-03T00:00:00.000Z"),

"items" : [

{

"product_name" : "Peach",

"quantity" : 4,

"price" : 1.49

},

{

"product_name" : "Pear",

"quantity" : 1,

"price" : 2.99

}

]

}

What is the query to retrieve the total sales for each customer in the orders collection?

A : db.orders.aggregate([ { $group: { _id: "$customer_name", total_sales: { $sum: { $multiply: ["$items.quantity", "$items.price"] } } } }])

B : db.orders.aggregate([ { $unwind: "$items" }, { $group: { _id: "$customer_name", total_sales: { $sum: { $multiply: ["$items.quantity", "$items.price"] } } } }])

C : db.orders.aggregate([ { $unwind: "$items" }, { $group: { total_sales: { $sum: { $multiply: ["$items.quantity", "$items.price"] } } } }])

D : db.orders.aggregate([ { $unwind: "$items" }, { $group: { _id: "$customer_name", total_sales: { $sum: { $multiply: ["quantity", "price"] } } } }])

Answer: B

Question 2

You have a MongoDB collection named students, where each document represents a student. The documents include fields like name, age, and courses. The courses field is an array that lists the course codes each student is enrolled in. You want to find all students enrolled in the course with the code "CS101". Which query will correctly retrieve all students enrolled in "CS101"?

A : db.students.find({ "courses": { $elemMatch: { $eq: "CS101" } } })

B : db.students.find({ "courses": [ "CS101" ] })

C : db.students.find({ "courses": { $in: "CS101" } })

D : db.students.find({ "courses": "CS101" })

Answer: D

Question 3

In the context of MongoDB development, you are creating a new service that is expected to handle high load. You've decided to leverage the MongoDB drivers' connection pooling capability. What best describes connection pooling and its advantages?

A : Connection pooling ensures secure data transfer between the client and the server by encrypting all connections in the pool.

B : Connection pooling allows a client to connect to a database only when a data transfer operation occurs, reducing the memory requirements.

C : Connection pooling involves maintaining a "pool" of open database connections, which can be reused for multiple requests, improving performance.

D : Connection pooling refers to the storage of multiple connection strings for redundancy, increasing system reliability.

Answer: C

Question 4

In MongoDB, which statement accurately describes a typical task for a MongoDB developer related to schema validation?

A : Schema validation enables the creation of complex relationships between multiple collections.

B : Schema validation guarantees the uniqueness of a specific field across all documents in a collection.

C : Schema validation ensures the presence of required fields and their data types within a document.

D : Schema validation provides real-time data synchronization across multiple MongoDB instances.

Answer: C

Question 5

Suppose you have a restaurants collection with the following document structure:

{

_id: ObjectId("5eb3d668b31de5d588f42931"),

address: {

building: '6409',

coord: [ -74.00528899999999, 40.628886 ],

street: '11 Avenue',

zipcode: '11219'

},

borough: 'Brooklyn',

cuisine: 'American',

grades: [

{

date: ISODate("2014-07-18T00:00:00.000Z"),

grade: 'A',

score: 12

},

{

date: ISODate("2013-07-30T00:00:00.000Z"),

grade: 'A',

score: 12

},

{

date: ISODate("2013-02-13T00:00:00.000Z"),

grade: 'A',

score: 11

},

{ date: ISODate("2012-08-16T00:00:00.000Z"),

grade: 'A',

score: 2 },

{

date: ISODate("2011-08-17T00:00:00.000Z"),

grade: 'A',

score: 11

}

],

name: 'Regina Caterers',

restaurant_id: '40356649'

}

You don't have any indexes so far. What will the query plan look like for the following query?

db.restaurants.find( { "cuisine": "American" } )

A : IXSCAN

B : SORT

C : FETCH

D : COLLSCAN

Answer: D