Free MongoDB C100DEV Exam Questions

Question 1

What is the difference between a sparse index and a non-sparse index in MongoDB?

A : A sparse index only indexes documents that contain the indexed field, while a non-sparse index indexes all documents in the collection, including those that do not contain the indexed field.

B : A sparse index indexes all documents in the collection, including those that contain the indexed field, while a non-sparse index only indexes documents that do not contain the indexed field.

C : A sparse index only indexes documents that do not contain the indexed field, while a non-sparse index indexes all documents in the collection, including those that contain the indexed field.

D : A sparse index indexes all documents in the collection, including those that do not contain the indexed field, while a non-sparse index only indexes documents that contain the indexed field.

Answer: A

Question 2

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 3

How can you insert a new document into a MongoDB collection named customers?

A :

B :

C :

D :

Answer: D

Question 4

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

{

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

"product_name": "Smartphone",

"brand": "Apple",

"price": 800,

"categories": [ "Electronics", "Smartphones" ],

"reviews": [

{ "username": "user1", "rating": 4, "comment": "Great product!" },

{ "username": "user2", "rating": 5, "comment": "Excellent!" },

{ "username": "user3", "rating": 3, "comment": "Good but overpriced." }

]

}

Select the MongoDB aggregation pipeline that returns the average rating of all products grouped by brand. The result should include only brands with an average rating greater than or equal to 4. The output should have the following format:

A : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }]

B : [ { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

C : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

D : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

Answer: D

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