Question 1

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?

Options :

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 2

In your database there is a collection named companies with the following document structure:

{

name: 'Wize',

relationships: [

{

is_past: false,

title: 'Head of Product',

person: {

first_name: 'Ethan',

last_name: 'Smith',

permalink: 'ethan-smith'

}

},

{

is_past: true,

title: 'Director, Business Development',

person: {

first_name: 'Stephanie',

last_name: 'Quay',

permalink: 'stephanie-quay'

}

},

{

is_past: true,

title: 'Sr. Engineer',

person: {

first_name: 'Stefan',

last_name: 'Antonowicz',

permalink: 'stefan-antonowicz'

}

]

}

Which of the following queries should you use to extract all companies that have "Co-Founder" title in relationships field (Array)?

Options :

A : db.companies.find( { relationships: { $expr: { title: 'Co-Founder' } } } )

B : db.companies.find( { relationships: { $elemMatch: { title: 'Co-Founder' } } } )

C : db.companies.find( { relationships: { $match: { title: 'Co-Founder' } } } )

D : db.companies.find( { relationships: { $eq: { title: 'Co-Founder' } } } )

Answer: B

Question 3

Given the following documents in a collection:

{ _id: 1, n: [1,2,5], p: 0.75, c: 'Green' },

{ _id: 2, n: 'Orange', p: 'Blue', c: 42, q: 14 },

{ _id: 3, n: [1,3,7], p: 0.85, c: 'Orange' }

Which two documents can successfully be added in the same collection? (Select two)

Options :

A : { _id: 1, n: [1,2,5], p: 0.75, c: 'Green' }

B : { _id: 2, n: [1,2,5], p: 0.75, c: 'Green' }

C : { _id: 6, n: [1,3,7], p: 0.85, c: 'Orange }

D : { _id: 5, n: [1,2,5], p: 0.75, c: 'Green' }

Answer: C,D

Question 4

Which of the following stages do you need to use to randomly select 10 documents from a specific collection?

Options :

A : { $rand: { size: 10 } }

B : { $sample: { get: 10 } }

C : { $sampleRate: { size: 10 } }

D : { $sample: { size: 10 } }

Answer: D

Question 5

A MongoDB collection named logs is used to store event data for a web application. The collection originally had indexes on the timestamp, user_id, and event_type fields to facilitate fast queries on these fields. Due to a recent data archiving strategy, you decide to remove the index on event_type to save storage space. However, after doing so, some queries on the logs collection have slowed down considerably, especially those filtering by event_type. What is the most likely ramification of deleting the index on the event_type field?

Options :

A : Removing the index has no impact on the performance of queries filtering by event_type.

B : The database size will increase because the data is now unindexed.

C : All queries involving the event_type field will now use the _id index, resulting in faster performance.

D : Queries involving the event_type field will require a full collection scan, leading to slower performance.

Answer: D

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