Free MongoDB C100DEV Exam Questions

Question 1

You are designing a MongoDB collection to store information about books in a library. Each book document should contain the following fields:title: The title of the book (string).authors: An array of author names (array of strings).published_date: The publication date of the book (ISODate).categories: An array of categories or genres the book belongs to (array of strings).copies: An array containing details about each copy of the book, where each copy has an availability_status (string), location (string), and borrowed_date (ISODate) if the book is currently borrowed.Given this requirement, which of the following schemas correctly represents the document model for this use case? (Select two)

A :

B :

C :

D :

E :

Answer: A,E

Question 2

You are developing a MongoDB application where write performance is a critical requirement. Given a scenario where your application primarily performs insert operations and rarely performs updates or deletes, which of the following MongoDB features would best optimize this heavy-write workload?

A : Change Streams

B : Capped Collections

C : Aggregation Pipeline

D : Sharding

Answer: D

Question 3

Which of the following scenarios is best suited for applying the Attribute Pattern?

A : Product categories in the online store.

B : You need to apply document validation before inserting/modifying.

C : The documents in the collection are quite large.

D : Some fields share a similar characteristics, and you want to search across those fields.

Answer: D

Question 4

We have an accounts collection with the following document structure:

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239d"),

account_id: 864905,

limit: 10000,

products: [ 'Commodity', 'InvestmentStock' ]

},

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239e"),

account_id: 299072,

limit: 10000,

products: [ 'InvestmentFund', 'InvestmentStock' ]

},

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239f"),

account_id: 137994,

limit: 10000,

products: [ 'CurrencyService', 'InvestmentStock' ]

}

We need to use Aggregation Framework to find the distribution of products field. Sort the result set by decreasing total number of products.

Expected output:

[

{ _id: 'InvestmentStock', total: 1746 },

{ _id: 'CurrencyService', total: 742 },

{ _id: 'Brokerage', total: 741 },

{ _id: 'InvestmentFund', total: 728 },

{ _id: 'Commodity', total: 720 },

{ _id: 'Derivatives', total: 706 }

]

Which pipeline should you use?

A : [{ $group: { _id: "$products", total: { $sum: 1 } }}]

B : [{ $unwind: { path: "$products" }}, { $group: { _id: "$products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

C : [{ $unwind: { path: "products" }}, { $group: { _id: "products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

D : [{ $group: { _id: "$products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

Answer: B

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