Question 1

You are developing a MongoDB application where write performance is a critical requirement. Given a scenario where your application primarily performs insert operations and rarely performs updates or deletes, which of the following MongoDB features would best optimize this heavy-write workload?

Options :

A : Change Streams

B : Capped Collections

C : Aggregation Pipeline

D : Sharding

Answer: D

Question 2

We have the following indexes:

{ name: 1, founded_year: 1 }

{ tag_list: 1, is_active: 1 }

And the following documents:

{

_id: ObjectId("52cdef7c4bab8bd675297daa"),

name: "Sparter",

founded_year: 2007,

tag_list: ["gaming", "game", "wow"],

is_active: true

},

{

_id: ObjectId("52cdef7c4bab8bd675297da3"),

name: "Yahoo!",

founded_year: 1994,

tag_list: ["search", "webmail"],

is_active: true

}

Select the true statement.

Options :

A : There would be a total of 2 index keys created across all of these documents and indexes.

B : There would be a total of 7 index keys created across all of these documents and indexes.

C : There would be a total of 4 index keys created across all of these documents and indexes.

D : The number of index keys cannot be determined from the given data.

Answer: B

Question 3

Which of the following command skips the first 10 documents in the movies collection and returns all remaining documents?

Options :

A : db.movies.find().limit(10)

B : db.movies.find().sort(10)

C : db.movies.find().skip(5)

D : db.movies.find().skip(10)

Answer: D

Question 4

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 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?

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

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