Free MongoDB C100DEV Exam Questions

Question 1

Given the following orders collection in MongoDB:

[

{

"_id": 1,

"customer": "John",

"orderDetails": [

{ "productId": "P1", "quantity": 2, "unitPrice": 50 },

{ "productId": "P2", "quantity": 1, "unitPrice": 30 }

]

},

{

"_id": 2,

"customer": "Sara",

"orderDetails": [

{ "productId": "P1", "quantity": 3, "unitPrice": 50 },

{ "productId": "P3", "quantity": 2, "unitPrice": 20 }

]

},

{

"_id": 3,

"customer": "Peter",

"orderDetails": [

{ "productId": "P2", "quantity": 2, "unitPrice": 30 },

{ "productId": "P4", "quantity": 1, "unitPrice": 100 }

]

}

]

Which MongoDB query will find all orders that contain a product with the productId of "P1" and a quantity greater than 1?

A : db.orders.find( { orderDetails: { $elemMatch: { productId: "P1", quantity: { $gt: 1 } } } } )

B : db.orders.find( { "orderDetails.productId": "P1", "orderDetails.quantity": { $gt: 1 } } )

C : db.orders.find( { orderDetails: { $elemMatch: { productId: "P1", quantity: { $gte: 1 } } } } )

D : db.orders.find( { "orderDetails.productId": "P1", "orderDetails.quantity": { $eq: 2 } } )

Answer: A

Question 2

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?

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

Question 3

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

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

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