Free MongoDB C100DEV Exam Questions

Question 1

What is the difference between a sparse index and a non-sparse index in MongoDB?

A : A sparse index only indexes documents that contain the indexed field, while a non-sparse index indexes all documents in the collection, including those that do not contain the indexed field.

B : A sparse index indexes all documents in the collection, including those that contain the indexed field, while a non-sparse index only indexes documents that do not contain the indexed field.

C : A sparse index only indexes documents that do not contain the indexed field, while a non-sparse index indexes all documents in the collection, including those that contain the indexed field.

D : A sparse index indexes all documents in the collection, including those that do not contain the indexed field, while a non-sparse index only indexes documents that contain the indexed field.

Answer: A

Question 2

Consider a MongoDB database containing a collection of documents representing online orders for an e-commerce website. The documents have the following structure:

{

"_id": ObjectId("5f95a1d11a12b400001b75c0"),

"order_number": "ORD-001",

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

"customer": {

"name": "John Doe",

"email": "johndoe@example.com"

},

"items": [

{ "product_name": "Smartphone", "quantity": 2, "price": 800 },

{ "product_name": "Laptop", "quantity": 1, "price": 1500 }

],

"shipping_address": {

"street": "123 Main St",

"city": "New York",

"state": "NY",

"zipcode": "10001"

},

"total_amount": 3000

}

Select an aggregation pipeline that returns the total sales amount by state and month, for orders placed in the year 2022. The result should have the following format:

A : [ { "$match": { "order_date": { "$gte": ISODate("2022-01-01T00:00:00.000Z"), "$lt": ISODate("2023-01-01T00:00:00.000Z") } } }, { "$group": { "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } }, "sales_amount": { "$sum": "$total_amount" } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

B : [ { "$group": { "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } }, "sales_amount": { "$sum": "$total_amount" } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

C : [ { "$match": { "order_date": { "$gte": ISODate("2022-01-01T00:00:00.000Z"), "$lt": ISODate("2023-01-01T00:00:00.000Z") } } }, { "$group": { "sales_amount": { "$sum": "$total_amount" } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

D : [ { "$match": { "order_date": { "$gte": ISODate("2022-01-01T00:00:00.000Z"), "$lt": ISODate("2023-01-01T00:00:00.000Z") } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

Answer: A

Question 3

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 4

Consider a collection named students with the following document:

{

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

"name" : "Alice",

"age" : 21,

"courses" : [

{

"name" : "Math",

"grade" : 89

},

{

"name" : "Science",

"grade" : 92

}

]

}

What is the query to update the grade of the "Math" course for the student with "_id" equal to ObjectId("5f0a7e80d8c9c7b5a48c49e1") to 95?

A : db.students.updateOne( { "courses.name": "Math" }, { $set: { "courses.$.grade": 95 } })

B : db.students.updateOne( { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1") }, { $set: { "courses.$.grade": 95 } })

C : db.students.updateOne( { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1"), "courses.name": "Math" }, { $set: { "courses.$.grade": 95 } })

D : db.students.updateOne( { _id: ObjectId("5f0a7e80d8c9c7b5a48c49e1"), "courses.name": "Math" }, { $set: { "grade": 95 } })

Answer: C

Question 5

In MongoDB, the $out operator is commonly used within an aggregation pipeline. Which of the following statements accurately describes the functionality and usage of the $out operator?

A : $out operator is used to output the results to a specified collection.

B : $out operator can be used to output the results to a collection in a different MongoDB database.

C : $out operator is used to export data directly to a file.

D : $out operator is used to read data from multiple collections in a single command.

Answer: A