Free MongoDB C100DEV Exam Questions

Question 1

Consider a MongoDB database managing e-commerce product data. The database has a collection named products with documents containing the fields product_id, category, price, and reviews. Each reviews field is an array of subdocuments with fields user_id, rating, and comment. You are tasked with optimizing a query that retrieves all products within a specific category that have an average rating higher than 4. The query is frequently used, and performance is critical. Which indexing strategy would best optimize this query?

A : Create a compound index on category and reviews.rating, with a partial filter expression that only includes documents where the average rating in reviews.rating is greater than 4.

B : Create a multi-key index on reviews.rating.

C : Create a compound index on category and reviews.rating.

D : Create a single index on the category field.

E : Create a compound index on category, reviews.rating, and price.

Answer: A

Question 2

Consider the following documents in a MongoDB collection orders:

{

"_id": 1,

"customer_id": 101,

"item": "apple",

"price": 0.5,

"quantity": 100,

"date": ISODate("2022-01-01T00:00:00Z")

},

{

"_id": 2,

"customer_id": 102,

"item": "banana",

"price": 0.25,

"quantity": 50,

"date": ISODate("2022-01-02T00:00:00Z")

},

{

"_id": 3,

"customer_id": 101,

"item": "orange",

"price": 0.75,

"quantity": 75,

"date": ISODate("2022-01-03T00:00:00Z")

}

What is the aggregation pipeline to group the documents by "customer_id" and calculate the sum of the "quantity" field for each group, sorted in descending order by the sum of the "quantity" field?

A : db.runCommand({ aggregate: "orders", group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } }, sort: { total_quantity: -1 }})

B : db.orders.aggregate([ { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } }, { $sort: { total_quantity: 1 } }])

C : db.orders.aggregate([ { $groupBy: { customer_id: "$customer_id" }, total_quantity: { $sum: "$quantity" } }, { $sort: { total_quantity: 1 } }])

D : db.orders.aggregate([ { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } }, { $sort: { total_quantity: -1 } }])

Answer: D

Question 3

Consider the following situation: You are a MongoDB Developer and are currently working on an application that requires complex reporting on large amounts of data. The application stores its data in MongoDB, which has grown significantly. To maintain efficiency and manageability, you decide to use MongoDB views. You want to create a view "UsersView" that includes only username, email, and createdAt fields from the Users collection. Which of the following commands would you use?

A : db.createView("UsersView", "Users", [{$project: {username: 1, email: 1, createdAt: 1}}])

B : db.Users.createView("UsersView", {$project: {username: 1, email: 1, createdAt: 1}})

C : db.Users.createView("UsersView", "Users", [{$project: {username: 1, email: 1, createdAt: 1}}])

D : db.Users.createView("UsersView", [{$project: {username: 1, email: 1, createdAt: 1}}])

Answer: A

Question 4

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 5

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