Free MongoDB C100DEV Exam Questions

Question 1

You have a MongoDB collection named employees that contains documents with fields name, department, and salary. You want to update the salary of all employees in the "Marketing" department to $75,000. The original collection looks like this:

[

{ "_id": 1, "name": "Alice", "department": "Marketing", "salary": 60000 },

{ "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 },

{ "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 62000 },

{ "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }

]

You run the following MongoDB update operation:

db.employees.updateMany(

{ "department": "Marketing" },

{ $set: { "salary": 75000 } }

)

What will the collection look like after this update operation?

A : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 60000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 75000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

B : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 62000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 62000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

C : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 75000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 75000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

D : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 60000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 75000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 75000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

Answer: C

Question 2

Which of the following statements are true regarding the MongoDB Aggregation Framework and pipelines? (Select two)

A : Aggregation pipelines can include stages like $match, $group, $project, and $sort.

B : Aggregation pipelines allow you to modify the structure of the original collection.

C : Aggregation pipelines support SQL-like joins between multiple collections.

D : The $limit stage is used to skip a specified number of documents in the aggregation pipeline.

E : The $unwind stage is used to deconstruct an array field from the input documents, creating a new document for each element of the array.

Answer: A,E

Question 3

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 4

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 5

Consider a collection named students with the following document:

{

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

"name" : "Alice",

"age" : 21,

"courses" : [

{

"name" : "Math",

"grade" : 89

},

{

"name" : "Science",

"grade" : 92

}

]

}

What is the query to update the grade of the "Math" course for the student with "_id" equal to ObjectId("5f0a7e80d8c9c7b5a48c49e1") to 95?

A : db.students.updateOne( { "courses.name": "Math" }, { $set: { "courses.$.grade": 95 } })

B : db.students.updateOne( { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1") }, { $set: { "courses.$.grade": 95 } })

C : db.students.updateOne( { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1"), "courses.name": "Math" }, { $set: { "courses.$.grade": 95 } })

D : db.students.updateOne( { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1"), "courses.name": "Math" }, { $set: { "grade": 95 } })

Answer: C