Question 1

Consider a MongoDB database containing a collection of documents representing online orders for an e-commerce website. The documents have the following structure:

{

"_id": ObjectId("5f95a1d11a12b400001b75c0"),

"order_number": "ORD-001",

"order_date": ISODate("2022-01-01T00:00:00.000Z"),

"customer": {

"name": "John Doe",

"email": "[email protected]"

},

"items": [

{ "product_name": "Smartphone", "quantity": 2, "price": 800 },

{ "product_name": "Laptop", "quantity": 1, "price": 1500 }

],

"shipping_address": {

"street": "123 Main St",

"city": "New York",

"state": "NY",

"zipcode": "10001"

},

"total_amount": 3000

}

Select an aggregation pipeline that returns the total sales amount by state and month, for orders placed in the year 2022. The result should have the following format:

Options :

A :

[
   {
      "$match": {
         "order_date": {
            "$gte": ISODate("2022-01-01T00:00:00.000Z"),
            "$lt": ISODate("2023-01-01T00:00:00.000Z")
         }
      }
   },
   {
      "$group": {
         "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } },
         "sales_amount": { "$sum": "$total_amount" }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

B :

[
   {
      "$group": {
         "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } },
         "sales_amount": { "$sum": "$total_amount" }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

C :

[
   {
      "$match": {
         "order_date": {
            "$gte": ISODate("2022-01-01T00:00:00.000Z"),
            "$lt": ISODate("2023-01-01T00:00:00.000Z")
         }
      }
   },
   {
      "$group": {
         "sales_amount": { "$sum": "$total_amount" }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

D :

[
   {
      "$match": {
         "order_date": {
            "$gte": ISODate("2022-01-01T00:00:00.000Z"),
            "$lt": ISODate("2023-01-01T00:00:00.000Z")
         }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

Answer: A

Question 2

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 3

What is the correct syntax to insert multiple documents into a MongoDB collection named orders and return the _id values for all inserted documents?

Options :

A :

B :

C :

D :

Answer: B

Question 4

Given the following documents in a collection:

{ _id: 1, n: [1,2,5], p: 0.75, c: 'Green' },

{ _id: 2, n: 'Orange', p: 'Blue', c: 42, q: 14 },

{ _id: 3, n: [1,3,7], p: 0.85, c: 'Orange' }

Which two documents can successfully be added in the same collection? (Select two)

Options :

A : { _id: 1, n: [1,2,5], p: 0.75, c: 'Green' }

B : { _id: 2, n: [1,2,5], p: 0.75, c: 'Green' }

C : { _id: 6, n: [1,3,7], p: 0.85, c: 'Orange }

D : { _id: 5, n: [1,2,5], p: 0.75, c: 'Green' }

Answer: C,D

Question 5

In the context of MongoDB development, you are creating a new service that is expected to handle high load. You've decided to leverage the MongoDB drivers' connection pooling capability. What best describes connection pooling and its advantages?

Options :

A : Connection pooling ensures secure data transfer between the client and the server by encrypting all connections in the pool.

B : Connection pooling allows a client to connect to a database only when a data transfer operation occurs, reducing the memory requirements.

C : Connection pooling involves maintaining a "pool" of open database connections, which can be reused for multiple requests, improving performance.

D : Connection pooling refers to the storage of multiple connection strings for redundancy, increasing system reliability.

Answer: C

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