Question 1

In a MongoDB database, you have a collection named orders that contains information about various product orders placed by customers. Each document in the collection has the following structure:

{

"customerID": "",

"orderID": "",

"product": "",

"quantity": ,

"price": ,

"date": "",

"status": ""

}

You are required to write an aggregation query that retrieves all orders placed by customers where the quantity of ordered items is greater than 100 and the status of the order is "delivered". Which of the following $match stages would you use to achieve this? Select the best option.

Options :

A : { "$match": { "quantity": { "$gt": 100 }, "status": "delivered" } }

B : { "$match": { "quantity": { "$lt": 100 }, "status": "delivered" } }

C : { "$match": { "quantity": { "$gt": 100 }, "status": { "$eq": "delivered" } } }

D : { "$match": { "quantity": { "$gte": 100 }, "status": "delivered" } }

Answer: A

Question 2

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

Question 3

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 4

Which of the following scenarios is best suited for applying the Attribute Pattern?

Options :

A : Product categories in the online store.

B : You need to apply document validation before inserting/modifying.

C : The documents in the collection are quite large.

D : Some fields share a similar characteristics, and you want to search across those fields.

Answer: D

Question 5

Suppose you have a reviews collection with the following index:

{ stars: 1, votes: 1 }

Which of the following queries would be a covered query?

Options :

A :

db.reviews.find(
    { votes: { $gte: 100 } },
    { _id: 0, votes: 1, stars: 1 }
)

B :

db.reviews.find(
    { votes: { $gte: 100 } },
    { _id: 0, votes: 1 }
)

C :

db.reviews.find(
    { votes: { $gte: 100 }, stars: 4.5 },
    { _id: 0 }
)

D :

db.reviews.find(
    { stars: { $gt: 4.5 } },
    { _id: 0, votes: 1 }
)

Answer: D