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Question 1

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.

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 2

You are tasked with developing a high-performance application using MongoDB as your primary database. The application heavily relies on queries that retrieve user profiles based on user names and their location. Given this requirement, you decide to implement a strategy to make these queries more efficient. What strategy should you implement to ensure these queries are covered queries?

A : Use the $project operator in the aggregation pipeline to include only the username and location fields in the result set.

B : Create separate indexes on the username and location fields and include only these fields in the queries.

C : Use the $match operator in the aggregation pipeline to filter documents based on username and location.

D : Create a compound index on the username and location fields and project only these two fields in the queries.

Answer: D

Question 3

Given a movies collection where each document has the following structure:

{

_id: ObjectId('573a1390f29313caabcd60e4'),

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

title: 'The Immigrant',

year: 1917,

imdb: { rating: 7.8, votes: 4680, id: 8133 },

countries: [ 'USA' ]

}

Which of the following queries will find all the movies that have more votes than the year in which they were released?

A : db.movies.find({ $expr: { $gt: ["imdb.votes", "year"] } } )

B : db.movies.find({ $expr: { $lt: ["$imdb.votes", "$year"] } } )

C : db.movies.find({ $gt: ["$imdb.votes", "$year"] } )

D : db.movies.find({ $expr: { $gt: ["$imdb.votes", "$year"] } } )

Answer: D

Question 4

Consider a tasks collection that holds task records. The document structure is as follows:

{

"_id": ObjectId("64b64c58ed01c0a5e72dbf5f"),

"task": "Review PR",

"status": "pending",

"assignedTo": "alice"

}

Two operations are performed concurrently:

Operation A:

db.tasks.findAndModify({

query: { task: "Review PR", status: "pending" },

remove: true

});

Operation B:

db.tasks.findAndModify({

query: { task: "Review PR", status: "pending" },

update: { $set: { status: "completed" } },

new: true

});

Operation B starts slightly after Operation A but before Operation A completes. What will be the final state of the tasks collection after both operations have been executed?

A : The document will be removed from the collection.

B : The document's status will be updated to "completed".

C : A new document with the status "completed" will be inserted.

D : The document's status will remain "pending".

Answer: A

Question 5

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?

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

Name:	MongoDB Certified Developer Associate
Exam Code:	C100DEV
Certification:	MongoDB Certified Developer Associate
Vendor:	MongoDB
Total Questions:	411
Last Updated:	Sep 28, 2024

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