Question 1

Consider a collection named students with the following document:

{

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

"name" : "Alice",

"age" : 21,

"courses" : [

{

"name" : "Math",

"grade" : 89

},

{

"name" : "Science",

"grade" : 92

}

]

}

What is the query to update the grade of the "Math" course for the student with "_id" equal to ObjectId("5f0a7e80d8c9c7b5a48c49e1") to 95?

Options :

A :

db.students.updateOne(
   { "courses.name": "Math" },
   { $set: { "courses.$.grade": 95 } }
)

B :

db.students.updateOne(
   { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1") },
   { $set: { "courses.$.grade": 95 } }
)

C :

db.students.updateOne(
   { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1"), "courses.name": "Math" },
   { $set: { "courses.$.grade": 95 } }
)

D :

db.students.updateOne(
   { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1"), "courses.name": "Math" },
   { $set: { "grade": 95 } }
)

Answer: C

Question 2

Consider the following documents in a MongoDB collection orders:

{

"_id": 1,

"customer_id": 101,

"item": "apple",

"price": 0.5,

"quantity": 100,

"date": ISODate("2022-01-01T00:00:00Z")

},

{

"_id": 2,

"customer_id": 102,

"item": "banana",

"price": 0.25,

"quantity": 50,

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

},

{

"_id": 3,

"customer_id": 101,

"item": "orange",

"price": 0.75,

"quantity": 75,

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

}

What is the aggregation pipeline to group the documents by "customer_id" and calculate the sum of the "quantity" field for each group, sorted in descending order by the sum of the "quantity" field?

Options :

A :

db.runCommand({
  aggregate: "orders",
  group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } },
  sort: { total_quantity: -1 }
})

B :

db.orders.aggregate([
  { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } },
  { $sort: { total_quantity: 1 } }
])

C :

db.orders.aggregate([
  {
    $groupBy: { customer_id: "$customer_id" },
    total_quantity: { $sum: "$quantity" }
  },
  { $sort: { total_quantity: 1 } }
])

D :

db.orders.aggregate([
  { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } },
  { $sort: { total_quantity: -1 } }
])

Answer: D

Question 3

You are designing a MongoDB schema for an e-commerce platform. The platform has three main collections: Products, Orders, and Users.Products Collection: Contains information about each product, such as its name, price, and description.Orders Collection: Contains details about each order, including the userId (referencing the Users collection), an array of productIds (referencing the Products collection), and the total order amount.Users Collection: Contains user details, such as name, email, and address.Which of the following data modeling approaches represents an anti-pattern in MongoDB?

Options :

A : Referencing Orders within the Users collection by storing an array of orderIds.

B : Creating a separate ProductDetails collection and storing all product details there, referencing it from the Products collection.

C : Referencing productIds in the Orders collection instead of embedding full product details.

D : Embedding user details directly in the Orders collection.

Answer: B

Question 4

Suppose you have a restaurants collection with the following document structure:

{

_id: ObjectId("5eb3d668b31de5d588f42931"),

address: {

building: '6409',

coord: [ -74.00528899999999, 40.628886 ],

street: '11 Avenue',

zipcode: '11219'

},

borough: 'Brooklyn',

cuisine: 'American',

grades: [

{

date: ISODate("2014-07-18T00:00:00.000Z"),

grade: 'A',

score: 12

},

{

date: ISODate("2013-07-30T00:00:00.000Z"),

grade: 'A',

score: 12

},

{

date: ISODate("2013-02-13T00:00:00.000Z"),

grade: 'A',

score: 11

},

{ date: ISODate("2012-08-16T00:00:00.000Z"),

grade: 'A',

score: 2 },

{

date: ISODate("2011-08-17T00:00:00.000Z"),

grade: 'A',

score: 11

}

],

name: 'Regina Caterers',

restaurant_id: '40356649'

}

You don't have any indexes so far. What will the query plan look like for the following query?

db.restaurants.find( { "cuisine": "American" } )

Options :

A : IXSCAN

B : SORT

C : FETCH

D : COLLSCAN

Answer: D

Question 5

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

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