Question 1

Suppose you have a products collection with an index:

{ product_category: 1 }

For which of the following queries can MongoDB look at only a subset of the index entries, rather than all of the index entries?

Options :

A : db.products.find( { product_category: /A$/ } )

B : db.products.find( { product_category: /A./ } )

C : db.products.find( { product_category: /S./ } )

D : db.products.find( { product_category: /^A./ } )

Answer: D

Question 2

We have a movies collection with the following document structure:

{

_id: ObjectId("573a1390f29313caabcd6223"),

genres: [ 'Comedy', 'Drama', 'Family' ],

title: 'The Poor Little Rich Girl',

released: ISODate("1917-03-05T00:00:00.000Z"),

year: 1917,

imdb: { rating: 6.9, votes: 884, id: 8443 }

},

{

_id: ObjectId("573a13e3f29313caabdc08a4"),

genres: [ 'Horror', 'Thriller' ],

title: 'Mary Loss of Soul',

year: 2014,

imdb: { rating: '', votes: '', id: 2904798 }

}

We need to use Aggregation Framework to calculate the following aggregates:

average imdb rating

minimum imdb rating

maximum imdb rating

Expected output:

[

{

_id: null,

avg_rating: 6.6934040649367255,

min_rating: 1.6,

max_rating: 9.6

}

]

Please note that some documents have "" (empty string) for the field "imdb.rating". Exclude these documents before aggregation.

Which pipeline should you use?

Options :

A :

[{
    $match: {
        "imdb.rating": {
            $ne: ""
        }
    }
}, {
    $group: {
        _id: null,
        avg_rating: {
            $avg: "imdb.rating"
        },
        min_rating: {
            $min: "imdb.rating"
        },
        max_rating: {
            $max: "imdb.rating"
        }
    }
}]

B :

[{
    $group: {
        _id: null,
        avg_rating: {
            $avg: "$imdb.rating"
        },
        min_rating: {
            $min: "$imdb.rating"
        },
        max_rating: {
            $max: "$imdb.rating"
        }
    }
}]

C :

[{
    $match: {
        "imdb.rating": {
            $ne: ""
        }
    }
}, {
    $group: {
        _id: null,
        avg_rating: {
            $avg: "$imdb.rating"
        },
        min_rating: {
            $min: "$imdb.rating"
        },
        max_rating: {
            $max: "$imdb.rating"
        }
    }
}]

D :

[{
    $match: {
        "imdb.rating": {
            $ne: ""
        }
    }
}, {
    $group: {
        _id: null,
        avg_rating: {
            avg: "$imdb.rating"
        },
        min_rating: {
            min: "$imdb.rating"
        },
        max_rating: {
            max: "$imdb.rating"
        }
    }
}]

Answer: C

Question 3

Consider the following situation: You are a MongoDB Developer and are currently working on an application that requires complex reporting on large amounts of data. The application stores its data in MongoDB, which has grown significantly. To maintain efficiency and manageability, you decide to use MongoDB views. You want to create a view "UsersView" that includes only username, email, and createdAt fields from the Users collection. Which of the following commands would you use?

Options :

A : db.createView("UsersView", "Users", [{$project: {username: 1, email: 1, createdAt: 1}}])

B : db.Users.createView("UsersView", {$project: {username: 1, email: 1, createdAt: 1}})

C : db.Users.createView("UsersView", "Users", [{$project: {username: 1, email: 1, createdAt: 1}}])

D : db.Users.createView("UsersView", [{$project: {username: 1, email: 1, createdAt: 1}}])

Answer: A

Question 4

Suppose you're designing a MongoDB database for an online ticket booking system where millions of users can concurrently book tickets for various events. To maintain data consistency, you need to ensure that only one user can book a specific seat for an event at a time. Which MongoDB feature would you use to ensure data consistency in this high-concurrency situation?

Options :

A : Multi-document transactions

B : Change Streams

C : Capped Collections

D : Sharding

Answer: A

Question 5

Consider a MongoDB collection named products which has documents in the following format:

{

"_id" : ObjectId("5f5b0d2f3e3dfbcc11c84444"),

"name" : "Smartphone",

"brand" : "Apple",

"category" : "Electronics",

"price" : 1000,

"discount" : 0.1

}

What is the correct syntax to update the "price" field of all documents in the products collection by multiplying it with (1 - discount) using the $set operator and the $multiply operator?

Options :

A :

db.products.update(
  {},
  {
    $set: { price: { $multiply: ["$price", { $subtract: [1, "$discount"] }] } }
  }
)

B :

db.products.updateMany(
  {},
  { $set: { price: { $multiply: ["$price", 1 - $discount] } } }
)

C :

db.products.update(
  {},
  { $set: { price: { $multiply: ["$price", 1 - $discount] } } }
)

D :

db.products.updateMany(
  {},
  {
    $set: { price: { $multiply: ["$price", { $subtract: [1, "$discount"] }] } }
  }
)

Answer: D

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