Question 1

Where is the MongoDB configuration file usually located?

Options :

A :

B :

C :

D :

E :

Answer: B

Question 2

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 3

In MongoDB, which statement accurately describes a typical task for a MongoDB developer related to schema validation?

Options :

A : Schema validation enables the creation of complex relationships between multiple collections.

B : Schema validation guarantees the uniqueness of a specific field across all documents in a collection.

C : Schema validation ensures the presence of required fields and their data types within a document.

D : Schema validation provides real-time data synchronization across multiple MongoDB instances.

Answer: C

Question 4

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 5

You are developing a blogging platform using MongoDB as the database. Each blog post document has a tags array field that holds a list of strings. You are tasked to implement a search functionality that allows users to find blog posts based on the tags. Which of the following MongoDB features would you utilize to implement this functionality efficiently?

Options :

A : Capped Collections

B : Sharding

C : Text Indexes

D : Compound Indexes

E : GridFS

Answer: C

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