Question 1

You are a MongoDB Associate Developer and need to demonstrate how to load the Atlas Sample Dataset into your MongoDB Atlas cluster. Your manager has asked you to guide a junior developer through this process to ensure they can independently load the dataset. What steps should the junior developer follow to successfully load the Atlas Sample Dataset?

Options :

A : In MongoDB Atlas, navigate to the "Clusters" tab, click on the "Load Sample Dataset" button, and wait for the dataset to be fully loaded.

B : In MongoDB Compass, connect to your cluster, go to the "Import Data" tab, and import the sample dataset by uploading the sample-dataset.json file.

C : Use the MongoDB Atlas API to send a POST request to the /loadSampleDataset endpoint, specifying your cluster ID in the request body.

D : Open the MongoDB Shell, connect to your cluster, and run the command mongoimport --db sample --collection datasets --file sample-dataset.json.

Answer: A

Question 2

Consider a collection named orders with the following documents:

{

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

"order_number" : 1001,

"customer_id" : "CUST-001",

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

"total_amount" : 199.99

},

{

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

"order_number" : 1002,

"customer_id" : "CUST-002",

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

"total_amount" : 299.99

},

{

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

"order_number" : 1003,

"customer_id" : "CUST-003",

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

"total_amount" : 399.99

}

What is the query to create a compound index on the "customer_id" (ascending) and "order_date" (descending) fields in the orders collection?

Options :

A : db.orders.createIndex({ order_date: -1, customer_id: 1 })

B : db.orders.createIndex({ customer_id: asc, order_date: desc })

C : db.orders.createIndex({ customer_id: 1, order_date: -1 })

D : db.orders.createIndex({ customer_id: -1, order_date: 1 })

Answer: C

Question 3

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?

Options :

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 4

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?

Options :

A : Multi-document transactions

B : Change Streams

C : Capped Collections

D : Sharding

Answer: A

Question 5

Which of the following is the best practice for managing MongoDB data growth in a production environment (scale-out)?

Options :

A : Increasing the RAM size of the existing server.

B : Using replication.

C : Sharding the data across multiple servers.

D : Adding more disk space to the existing server.

Answer: C

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