Free MongoDB C100DEV Exam Questions

Question 1

Consider the following situation: You are a MongoDB Developer and are currently working on an application that requires complex reporting on large amounts of data. The application stores its data in MongoDB, which has grown significantly. To maintain efficiency and manageability, you decide to use MongoDB views. You want to create a view "UsersView" that includes only username, email, and createdAt fields from the Users collection. Which of the following commands would you use?

A : db.createView("UsersView", "Users", [{$project: {username: 1, email: 1, createdAt: 1}}])

B : db.Users.createView("UsersView", {$project: {username: 1, email: 1, createdAt: 1}})

C : db.Users.createView("UsersView", "Users", [{$project: {username: 1, email: 1, createdAt: 1}}])

D : db.Users.createView("UsersView", [{$project: {username: 1, email: 1, createdAt: 1}}])

Answer: A

Question 2

As a MongoDB Developer, you are tasked with the deletion of specific documents in a collection named users. The documents to be deleted are those that meet the following criteria: the age field is less than 18 and the status field is "inactive". After the deletion, you need to know the count of the deleted documents. Which of the following commands would correctly perform this operation?

A :

B :

C :

D :

E :

Answer: A

Question 3

A MongoDB collection named logs is used to store event data for a web application. The collection originally had indexes on the timestamp, user_id, and event_type fields to facilitate fast queries on these fields. Due to a recent data archiving strategy, you decide to remove the index on event_type to save storage space. However, after doing so, some queries on the logs collection have slowed down considerably, especially those filtering by event_type. What is the most likely ramification of deleting the index on the event_type field?

A : Removing the index has no impact on the performance of queries filtering by event_type.

B : The database size will increase because the data is now unindexed.

C : All queries involving the event_type field will now use the _id index, resulting in faster performance.

D : Queries involving the event_type field will require a full collection scan, leading to slower performance.

Answer: D

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

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