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

How can we present the basic syntax for aggregation in MongoDB?

A :

B :

C :

D :

Answer: D

Question 3

What is the best practice in using the $match operator?

A : It doesn't matter when we apply $match stage.

B : MongoDB does not have a $match stage.

C : The later in the pipeline, the better.

D : The earlier in the pipeline, the better. Not only because we will be using expression filters which reduce the number of documents to be processed, but also because we can use indexes.

Answer: D

Question 4

You have two collections in your MongoDB database: students and grades. The students collection contains the following document:

{

"_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"),

"student_id": 1,

"name": "Alice"

}

The grades collection contains the following documents:

{

"_id": ObjectId("64d5b0e5c9d95a76ac3ebed3"),

"student_id": 2,

"course": "Math",

"grade": "A"

},

{

"_id": ObjectId("64d5b0e5c9d95a76ac3ebed4"),

"student_id": 2,

"course": "Science",

"grade": "B"

}

You run the following aggregation query to retrieve students along with their grades:

db.students.aggregate([

{

$lookup: {

from: "grades",

localField: "student_id",

foreignField: "student_id",

as: "grades_details"

}

])

What will be the structure of the documents returned by this aggregation query for the student with student_id: 1?

A : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": null}

B : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": []}

C : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": [ { "course": "Math", "grade": "A" } ]}

D : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": [ { "course": "Math", "grade": "A" }, { "course": "Science", "grade": "B" } ]}

Answer: B

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