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

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

You are working on a MongoDB database for a library system. The system tracks book information in a Books collection. A typical document in this collection looks like this:

Original Document:

{

"_id": ObjectId("book_id"),

"title": "The Art of Computer Programming",

"author": "Donald Knuth",

"genres": ["Computer Science", "Algorithms"],

"published_year": 1968,

"copies_available": 3

}

You are asked to update the document by replacing it with the following document:

Updated Document:

{

"title": "The Art of Computer Programming",

"published_year": 1973

}

After this update, what will the final document in the database look like?

A : { "_id": ObjectId("book_id"), "title": "The Art of Computer Programming", "published_year": 1973}

B : { "_id": ObjectId("book_id"), "title": "The Art of Computer Programming", "author": "Donald Knuth", "genres": ["Computer Science", "Algorithms"], "published_year": 1973, "copies_available": 3}

C : { "title": "The Art of Computer Programming", "published_year": 1973, "author": "Donald Knuth"}

D : { "_id": ObjectId("book_id"), "title": "The Art of Computer Programming", "published_year": 1973, "genres": ["Computer Science"]}

Answer: A

Question 5

What is the best practice in using the $match operator?

A : It doesn't matter when we apply $match stage.

B : MongoDB does not have a $match stage.

C : The later in the pipeline, the better.

D : The earlier in the pipeline, the better. Not only because we will be using expression filters which reduce the number of documents to be processed, but also because we can use indexes.

Answer: D