Free MongoDB C100DEV Exam Questions

Question 1

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 2

You are designing a MongoDB schema for an e-commerce platform. The system has three collections: Products, Categories, and Reviews.Products Collection: Contains information about each product, including its name, price, and categoryId referencing the Categories collection.Categories Collection: Contains the category name and description.Reviews Collection: Contains user reviews for products, including the productId referencing the Products collection, userId, rating, and reviewText.Which of the following is the best approach to model the relationships between Products, Categories, and Reviews in MongoDB?

A : Reference Categories in the Products collection using categoryId and reference Products in the Reviews collection using productId.

B : Embed Categories directly within the Products collection.

C : Embed Reviews directly within the Products collection.

D : Reference Reviews in both the Products and Categories collections.

Answer: A

Question 3

How does MongoDB handle sorting on fields that contain missing values (i.e., values that are not present in all documents in the collection)?

A : The behavior of sorting on missing values depends on the version of MongoDB being used.

B : The sort fails and returns an error.

C : Missing values are sorted first.

D : Missing values are sorted last.

Answer: C

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

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