Free MongoDB C100DEV Exam Questions

Question 1

Consider a collection named orders with the following documents:

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e1"),

"order_id" : 101,

"customer_name" : "John Doe",

"order_date" : ISODate("2022-01-01T00:00:00.000Z"),

"items" : [

{

"product_name" : "Apple",

"quantity" : 2,

"price" : 0.99

},

{

"product_name" : "Banana",

"quantity" : 5,

"price" : 0.5

}

]

}

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e2"),

"order_id" : 102,

"customer_name" : "Jane Doe",

"order_date" : ISODate("2022-01-02T00:00:00.000Z"),

"items" : [

{

"product_name" : "Orange",

"quantity" : 3,

"price" : 0.79

},

{

"product_name" : "Mango",

"quantity" : 2,

"price" : 1.99

}

]

}

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e3"),

"order_id" : 103,

"customer_name" : "Bob Smith",

"order_date" : ISODate("2022-01-03T00:00:00.000Z"),

"items" : [

{

"product_name" : "Peach",

"quantity" : 4,

"price" : 1.49

},

{

"product_name" : "Pear",

"quantity" : 1,

"price" : 2.99

}

]

}

What is the query to retrieve the total sales for each customer in the orders collection?

A : db.orders.aggregate([ { $group: { _id: "$customer_name", total_sales: { $sum: { $multiply: ["$items.quantity", "$items.price"] } } } }])

B : db.orders.aggregate([ { $unwind: "$items" }, { $group: { _id: "$customer_name", total_sales: { $sum: { $multiply: ["$items.quantity", "$items.price"] } } } }])

C : db.orders.aggregate([ { $unwind: "$items" }, { $group: { total_sales: { $sum: { $multiply: ["$items.quantity", "$items.price"] } } } }])

D : db.orders.aggregate([ { $unwind: "$items" }, { $group: { _id: "$customer_name", total_sales: { $sum: { $multiply: ["quantity", "price"] } } } }])

Answer: B

Question 2

Consider a collection named orders with the following documents:

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e1"),

"order_number" : 1001,

"customer_id" : "CUST-001",

"order_date" : ISODate("2022-01-01T00:00:00Z"),

"total_amount" : 199.99

},

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e2"),

"order_number" : 1002,

"customer_id" : "CUST-002",

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

"total_amount" : 299.99

},

{

"_id" : ObjectId("5f0a7e80d8c9c7b5a48c49e3"),

"order_number" : 1003,

"customer_id" : "CUST-003",

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

"total_amount" : 399.99

}

What is the query to create a compound index on the "customer_id" (ascending) and "order_date" (descending) fields in the orders collection?

A : db.orders.createIndex({ order_date: -1, customer_id: 1 })

B : db.orders.createIndex({ customer_id: asc, order_date: desc })

C : db.orders.createIndex({ customer_id: 1, order_date: -1 })

D : db.orders.createIndex({ customer_id: -1, order_date: 1 })

Answer: C

Question 3

Suppose you have a reviews collection with the following index:

{ stars: 1, votes: 1 }

Which of the following queries would be a covered query?

A : db.reviews.find( { votes: { $gte: 100 } }, { _id: 0, votes: 1, stars: 1 })

B : db.reviews.find( { votes: { $gte: 100 } }, { _id: 0, votes: 1 })

C : db.reviews.find( { votes: { $gte: 100 }, stars: 4.5 }, { _id: 0 })

D : db.reviews.find( { stars: { $gt: 4.5 } }, { _id: 0, votes: 1 })

Answer: D

Question 4

Which of the following statements are true regarding the MongoDB Aggregation Framework and pipelines? (Select two)

A : Aggregation pipelines can include stages like $match, $group, $project, and $sort.

B : Aggregation pipelines allow you to modify the structure of the original collection.

C : Aggregation pipelines support SQL-like joins between multiple collections.

D : The $limit stage is used to skip a specified number of documents in the aggregation pipeline.

E : The $unwind stage is used to deconstruct an array field from the input documents, creating a new document for each element of the array.

Answer: A,E

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