Free MongoDB C100DEV Exam Questions

Question 1

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 2

In a MongoDB database, you have a collection named orders that contains information about various product orders placed by customers. Each document in the collection has the following structure:

{

"customerID": "",

"orderID": "",

"product": "",

"quantity": ,

"price": ,

"date": "",

"status": ""

}

You are required to write an aggregation query that retrieves all orders placed by customers where the quantity of ordered items is greater than 100 and the status of the order is "delivered". Which of the following $match stages would you use to achieve this? Select the best option.

A : { "$match": { "quantity": { "$gt": 100 }, "status": "delivered" } }

B : { "$match": { "quantity": { "$lt": 100 }, "status": "delivered" } }

C : { "$match": { "quantity": { "$gt": 100 }, "status": { "$eq": "delivered" } } }

D : { "$match": { "quantity": { "$gte": 100 }, "status": "delivered" } }

Answer: A

Question 3

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 4

In a MongoDB database with referenced relationships, what is the advantage of using a separate collection for each entity over using a single collection for all entities?

A : Improved maintainability and reduced complexity of updates to related entities.

B : Improved query performance and reduced complexity of updates to related entities.

C : Improved maintainability and increased scalability.

D : Improved query performance and increased scalability.

Answer: A

Question 5

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